1 Preamble
2 Packages
3 Data preparation
4 Analysis of consonant repetition (“CVC”)
5 Analysis of adjacent syllable repetition
6 Accuracy
7 Homonymy
8 Homonymy: Logistic analysis
9 Homonymy (Gen1-5)
10 Homonymy and sound repetition
- 10.1 Impact of homonymy on sound repetition
- 10.2 Sound repetition among non-homonyms
11 Word length and sound repetition

1 Preamble

These are GCA analyses and model comparisons run for the reduplication study. Originally in raw R script, now in R Notebook format. It saves tables as .doc files and plots as .pdf files. This one uses the new coding of phonological repeition implemented by Kenny.

2 Packages

library(ggplot2)
library(broom)
library(lme4)

Loading required package: Matrix

library(bbmle)

Loading required package: stats4

library(lmerTest)


Attaching package: ‘lmerTest’

The following object is masked from ‘package:lme4’:

    lmer

The following object is masked from ‘package:stats’:

    step

library(sjPlot)
library(plyr)

3 Data preparation

3.1 Data read-in

This reads in the new dataset that includes the new coding of syllable and consonant repetition. But the script replaces the old equivalents (Adj_B, New_Redup_B) with the new variables (SyllableRepetition, ConsonantRepetition).

df = read.table(file="data_june_2018_recoded2.csv", na.strings=c("", "NA"), sep=",", header=TRUE)
df = subset(df, select = -c(New_Redup_B, Adj_B)) 
colnames(df)[c(46, 47)] <- c("Adj_B", "New_Redup_B") 
colnames(df)[6] <- "Unique_language"
df$Trial_Iteration[is.na(df$Trial_Iteration)] <- 0 #make NAs 0 for next line to work
df <-subset(df,Trial_Iteration != 1)  # keep seed languages and second round of iterations
# df <- subset(df, Transmitter == 1) # uncomment to keep only the data from the participants whose language was used to train the following generation
df_director<-subset(df,Stage!='interactM') # includes seed languages
df_matcher<-subset(df,Stage=='interactM') # includes only generations 1 to 5, since there is no accuracy for seed languages
df_director_generations_1_to_5 <- subset(df_director, Generation > 0) #excludes seed languages

3.2 Sum-coding

contrasts(df_director$LexiconType) = contr.sum(2)
contrasts(df_director$GroupType) = contr.sum(2)
contrasts(df_matcher$LexiconType) = contr.sum(2)
contrasts(df_matcher$GroupType) = contr.sum(2)

3.3 Adding 3rd-order polynomials for GCA

df_director$timebin = df_director$Generation + 1
t = poly(unique(df_director$timebin), 3)
df_director[,paste("ot", 1:3, sep="")] <- t[df_director$timebin, 1:3]
df_matcher$timebin = df_matcher$Generation
t = poly(unique(df_matcher$timebin), 3)
df_matcher[,paste("ot", 1:3, sep="")] <- t[df_matcher$timebin, 1:3]

4 Analysis of consonant repetition (“CVC”)

4.1 CVC: Descriptive plot

groups = ddply(df_director, c("GroupType", "LexiconType", "GroupNumber"), summarise, n=length(Block))
groups$group_id = factor(LETTERS[1:24])
df_director_plot = merge(df_director, groups, all.x = TRUE)
ggplot(df_director_plot, aes(Generation, New_Redup_B, group = group_id, colour=GroupType, linetype=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="line") +
  ylab("Consonant repetition")+ 
  ggtitle('Consonant repetition by group type and lexicon size')

dev.print(pdf, 'Consonant repetition by group type - dog dinner.pdf')

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4.2 CVC: Full models

# CVC_third_order <- glmer(New_Redup_B ~ #maximal random effect structure, up to cubic term 
#                 (ot1+ot2+ot3) * GroupType * LexiconType + (1+ot1+ot2+ot3|Unique_Participant) + 
#                 (1+ot1+ot2+ot3 |Meaning) + (1+ot1+ot2+ot3|Pair), data=df_director, family=binomial, 
#                 control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
#CVC_second_order <- glmer(New_Redup_B ~ #maximal random effect structure, up to quadratic term 
#                 (ot1+ot2) * GroupType * LexiconType + (1+ot1+ot2|Unique_Participant) + 
#                 (1+ot1+ot2 |Meaning) + (1+ot1+ot2|Pair), data=df_director, family=binomial, 
#                 control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
#CVC_first_order <- glmer(New_Redup_B ~ #maximal random effect structure, up to linear term 
#                 ot1 * GroupType * LexiconType + (1+ot1|Unique_Participant) + (1+ot1 |Meaning) + 
#                 (1+ot1+ot2|Pair), data=df_director, family=binomial, 
#                 control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))

4.3 CVC: Reduced models

The full models above all result in singular fit. The models converge without singular fit when all the random slopes are removed and ‘Pair’ is also removed as a random effect.Below are models with these terms dropped.

CVC_third_order <- glmer(New_Redup_B ~ #maximal random effect structure, up to cubic term 
                  (ot1+ot2+ot3) * GroupType * LexiconType + (1|Unique_Participant) + (1|Meaning),
                  data=df_director, family=binomial, 
                  control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
CVC_second_order <- glmer(New_Redup_B ~ #maximal random effect structure, up to quadratic term 
                    (ot1+ot2) * GroupType * LexiconType + (1|Unique_Participant) + (1|Meaning),
                   data=df_director, family=binomial, 
                   control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
CVC_first_order <- glmer(New_Redup_B ~ #maximal random effect structure, up to linear term                          
                    ot1 * GroupType * LexiconType + (1|Unique_Participant) + (1|Meaning),
                    data=df_director, family=binomial, 
                    control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
summary(CVC_third_order)

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
 Family: binomial  ( logit )
Formula: New_Redup_B ~ (ot1 + ot2 + ot3) * GroupType * LexiconType + (1 |      Unique_Participant) + (1 | Meaning)
   Data: df_director
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

     AIC      BIC   logLik deviance df.resid 
  5012.0   5126.5  -2488.0   4976.0     4275 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.9153 -0.6408 -0.4376  0.8083  5.0050 

Random effects:
 Groups             Name        Variance Std.Dev.
 Unique_Participant (Intercept) 0.8121   0.9012  
 Meaning            (Intercept) 0.2797   0.5289  
Number of obs: 4293, groups:  Unique_Participant, 156; Meaning, 18

Fixed effects:
                             Estimate Std. Error z value Pr(>|z|)    
(Intercept)                 -0.797421   0.161469  -4.939 7.87e-07 ***
ot1                          0.488495   0.187113   2.611  0.00904 ** 
ot2                          0.060052   0.178632   0.336  0.73674    
ot3                         -0.111754   0.153346  -0.729  0.46614    
GroupType1                   0.182995   0.089418   2.047  0.04071 *  
LexiconType1                -0.023338   0.102203  -0.228  0.81938    
ot1:GroupType1               0.319587   0.126504   2.526  0.01153 *  
ot2:GroupType1              -0.024023   0.126953  -0.189  0.84992    
ot3:GroupType1              -0.149515   0.127569  -1.172  0.24118    
ot1:LexiconType1            -0.325435   0.187024  -1.740  0.08185 .  
ot2:LexiconType1             0.105347   0.178676   0.590  0.55546    
ot3:LexiconType1             0.043746   0.153343   0.285  0.77543    
GroupType1:LexiconType1      0.018755   0.089352   0.210  0.83374    
ot1:GroupType1:LexiconType1  0.017291   0.126475   0.137  0.89126    
ot2:GroupType1:LexiconType1 -0.031397   0.126867  -0.247  0.80454    
ot3:GroupType1:LexiconType1  0.007978   0.127512   0.063  0.95011    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


Correlation matrix not shown by default, as p = 16 > 12.
Use print(x, correlation=TRUE)  or
    vcov(x)        if you need it

summary(CVC_second_order)

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
 Family: binomial  ( logit )
Formula: New_Redup_B ~ (ot1 + ot2) * GroupType * LexiconType + (1 | Unique_Participant) +      (1 | Meaning)
   Data: df_director
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

     AIC      BIC   logLik deviance df.resid 
  5005.5   5094.6  -2488.8   4977.5     4279 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.9033 -0.6434 -0.4386  0.8047  5.0822 

Random effects:
 Groups             Name        Variance Std.Dev.
 Unique_Participant (Intercept) 0.8187   0.9048  
 Meaning            (Intercept) 0.2798   0.5289  
Number of obs: 4293, groups:  Unique_Participant, 156; Meaning, 18

Fixed effects:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                 -0.81723    0.16091  -5.079  3.8e-07 ***
ot1                          0.49050    0.16355   2.999  0.00271 ** 
ot2                          0.05608    0.15746   0.356  0.72173    
GroupType1                   0.20507    0.08470   2.421  0.01547 *  
LexiconType1                -0.02359    0.10120  -0.233  0.81569    
ot1:GroupType1               0.31018    0.12513   2.479  0.01318 *  
ot2:GroupType1              -0.01850    0.12584  -0.147  0.88315    
ot1:LexiconType1            -0.35122    0.16361  -2.147  0.03182 *  
ot2:LexiconType1             0.13020    0.15746   0.827  0.40829    
GroupType1:LexiconType1      0.02428    0.08469   0.287  0.77433    
ot1:GroupType1:LexiconType1  0.02287    0.12510   0.183  0.85494    
ot2:GroupType1:LexiconType1 -0.03474    0.12575  -0.276  0.78233    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) ot1    ot2    GrpTy1 LxcnT1 ot1:GT1 ot2:GT1 o1:LT1 o2:LT1 GT1:LT o1:GT1:
ot1         -0.086                                                                         
ot2          0.071 -0.372                                                                  
GroupType1  -0.317  0.000 -0.005                                                           
LexiconTyp1 -0.033  0.005  0.001  0.005                                                    
ot1:GrpTyp1 -0.177  0.258  0.140  0.416 -0.001                                             
ot2:GrpTyp1  0.153  0.134  0.299 -0.389  0.003  0.024                                      
ot1:LxcnTy1  0.005 -0.095  0.039  0.021 -0.135  0.002  -0.006                              
ot2:LxcnTy1 -0.001  0.040 -0.080 -0.018  0.115 -0.007  -0.002  -0.374                      
GrpTyp1:LT1  0.003  0.021 -0.018 -0.052 -0.504  0.008  -0.005   0.000 -0.005               
ot1:GT1:LT1  0.000  0.003 -0.007  0.008 -0.281 -0.097   0.016   0.258  0.140  0.416        
ot2:GT1:LT1  0.002 -0.005 -0.003 -0.005  0.246  0.016  -0.090   0.133  0.298 -0.389  0.023

summary(CVC_first_order)

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
 Family: binomial  ( logit )
Formula: New_Redup_B ~ ot1 * GroupType * LexiconType + (1 | Unique_Participant) +      (1 | Meaning)
   Data: df_director
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

     AIC      BIC   logLik deviance df.resid 
  4998.8   5062.5  -2489.4   4978.8     4283 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.9083 -0.6427 -0.4412  0.8085  5.0362 

Random effects:
 Groups             Name        Variance Std.Dev.
 Unique_Participant (Intercept) 0.8181   0.9045  
 Meaning            (Intercept) 0.2796   0.5288  
Number of obs: 4293, groups:  Unique_Participant, 156; Meaning, 18

Fixed effects:
                             Estimate Std. Error z value Pr(>|z|)    
(Intercept)                 -0.814661   0.158876  -5.128 2.93e-07 ***
ot1                          0.524337   0.145977   3.592 0.000328 ***
GroupType1                   0.194888   0.077314   2.521 0.011711 *  
LexiconType1                -0.020530   0.097939  -0.210 0.833964    
ot1:GroupType1               0.304013   0.123888   2.454 0.014130 *  
ot1:LexiconType1            -0.277303   0.145879  -1.901 0.057313 .  
GroupType1:LexiconType1      0.005124   0.077317   0.066 0.947161    
ot1:GroupType1:LexiconType1  0.007622   0.123872   0.062 0.950938    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) ot1    GrpTy1 LxcnT1 ot1:GT1 o1:LT1 GT1:LT
ot1         -0.108                                           
GroupType1  -0.288  0.125                                    
LexiconTyp1 -0.038  0.001  0.018                             
ot1:GrpTyp1 -0.189  0.355  0.454 -0.005                      
ot1:LxcnTy1  0.001 -0.095  0.024 -0.172 -0.008               
GrpTyp1:LT1  0.012  0.024 -0.080 -0.466  0.018   0.125       
ot1:GT1:LT1 -0.003 -0.008  0.018 -0.306 -0.095   0.355  0.454

4.4 CVC: Model comparison

CVC_model.names <- c("CVC first order","CVC second order","CVC third order")
CVC_summ.table <- do.call(rbind, lapply(list(CVC_first_order,CVC_second_order,CVC_third_order), broom::glance))
CVC_table.cols <- c("df.residual", "deviance", "AIC")
CVC_reported.table <- CVC_summ.table[CVC_table.cols]
names(CVC_reported.table) <- c("Resid.Df", "Resid.Dev", "AIC")
CVC_reported.table[['dAIC']] <-  with(CVC_reported.table, AIC - min(AIC))
CVC_reported.table[['AIC_weight']] <- with(CVC_reported.table, exp(- 0.5 * dAIC) / sum(exp(- 0.5 * dAIC)))
CVC_reported.table$AIC <- NULL
CVC_reported.table$AIC_weight <- round(CVC_reported.table$AIC_weight, 2)
CVC_reported.table$dAIC <- round(CVC_reported.table$dAIC, 1)
CVC_reported.table$Resid.Dev <- round(CVC_reported.table$Resid.Dev, 2)
row.names(CVC_reported.table) <- CVC_model.names

Setting row names on a tibble is deprecated.

View(CVC_reported.table)
sjPlot::tab_df(CVC_reported.table,
       file="model comparison CVC.doc")

4.5 CVC: Plots showing model fit of 1st and 2nd order models by group type

ggplot(df_director, aes(Generation, New_Redup_B, shape=GroupType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(CVC_first_order), linetype=GroupType),
               fun.y=mean, geom="line") +
  ylab("Consonant repetition")+ 
  ggtitle('Consonant repetition by group type - first order model')

dev.print(pdf, 'Consonant repetition by group type - first order model.pdf')

quartz_off_screen 
                2

ggplot(df_director, aes(Generation, New_Redup_B, color=GroupType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(CVC_second_order), linetype=GroupType),
               fun.y=mean, geom="line") +
  ylab("CVC repetition")+ 
  ggtitle('CVC repetition by group type - second order model')

dev.print(pdf,'CVC repetition by group type - second order model.pdf')

quartz_off_screen 
                2

# sjPlot::tab_df(round(coef(summary(CVC_first_order)),3),file='CVC_fixed_effects.doc')
# sjPlot::tab_df(as.numeric(VarCorr(CVC_first_order)),file='CVC_random_effects.doc')

4.6 CVC: Plots showing model fit of 1st and 2nd order models by lexicon type

ggplot(df_director, aes(Generation, New_Redup_B, shape=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(CVC_first_order), linetype=LexiconType),
               fun.y=mean, geom="line") +
  ylab("CVC repetition") + 
  ggtitle('CVC repetition by lexicon type - first order model')

dev.print(pdf,'CVC repetition by lexicon type - first order model.pdf')

quartz_off_screen 
                2

ggplot(df_director, aes(Generation, New_Redup_B, shape=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(CVC_second_order), linetype=LexiconType),
               fun.y=mean, geom="line") +
  ylab("CVC repetition") + 
  ggtitle('CVC repetition by lexicon type - second order model')

dev.print(pdf,'CVC repetition by lexicon type - second order model.pdf')

quartz_off_screen 
                2

5 Analysis of adjacent syllable repetition

5.1 Descriptive plot

ggplot(df_director_plot, aes(Generation, Adj_B, group = group_id, colour=GroupType, linetype=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="line") +
  ylab("Syllable repetition")+ 
  ggtitle('Syllable repetition by group type and lexicon size')

dev.print(pdf, 'Syllable repetition by group type - dog dinner.pdf')

quartz_off_screen 
                2

5.2 Syllable repetition: Full models

# ADJ_third_order <- glmer(Adj_B ~ #maximal random effect structure, up to cubic term 
#                   (ot1+ot2+ot3) * GroupType * LexiconType + (1+ot1+ot2+ot3|Unique_Participant) + 
#                   (1+ot1+ot2+ot3 |Meaning) + (1+ot1+ot2+ot3|Pair), data=df_director, family=binomial, 
#                   control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
# ADJ_second_order <- glmer(Adj_B ~ #maximal random effect structure, up to quadratic term 
#                   (ot1+ot2) * GroupType * LexiconType + (1+ot1+ot2|Unique_Participant) + 
#                   (1+ot1+ot2 |Meaning) + (1+ot1+ot2|Pair),
#                   data=df_director, family=binomial, 
#                   control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
# ADJ_first_order <- glmer(Adj_B ~ #maximal random effect structure, up to linear term 
#                   ot1 * GroupType * LexiconType + (1+ot1|Unique_Participant) + 
#                   (1+ot1 |Meaning) + (1+ot1|Pair), data=df_director, family=binomial, 
#                   control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))

5.3 Syllable repetition: Reduced models

The full models above also result in singular fit. So they are reduced below.

ADJ_third_order <- glmer(Adj_B ~ #maximal random effect structure, up to cubic term 
                         (ot1+ot2+ot3) * GroupType * LexiconType + (1|Unique_Participant) + 
                           (1|Meaning), data=df_director, family=binomial, 
                         control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
ADJ_second_order <- glmer(Adj_B ~ #maximal random effect structure, up to quadratic term 
                      (ot1+ot2) * GroupType * LexiconType + (1|Unique_Participant) + 
                        (1|Meaning), data=df_director, family=binomial, 
                      control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
ADJ_first_order <- glmer(Adj_B ~ #maximal random effect structure, up to linear term 
                        ot1 * GroupType * LexiconType + (1|Unique_Participant) + 
                          (1|Meaning), data=df_director, family=binomial, 
                        control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
summary(ADJ_third_order)

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
 Family: binomial  ( logit )
Formula: Adj_B ~ (ot1 + ot2 + ot3) * GroupType * LexiconType + (1 | Unique_Participant) +      (1 | Meaning)
   Data: df_director
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

     AIC      BIC   logLik deviance df.resid 
  4218.0   4332.6  -2091.0   4182.0     4275 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.5872 -0.5279 -0.3722 -0.2213  5.3613 

Random effects:
 Groups             Name        Variance Std.Dev.
 Unique_Participant (Intercept) 0.9826   0.9913  
 Meaning            (Intercept) 0.2226   0.4718  
Number of obs: 4293, groups:  Unique_Participant, 156; Meaning, 18

Fixed effects:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                 -1.53427    0.15990  -9.595  < 2e-16 ***
ot1                          0.58571    0.20926   2.799  0.00513 ** 
ot2                         -0.04126    0.19946  -0.207  0.83614    
ot3                          0.00851    0.17095   0.050  0.96030    
GroupType1                   0.11537    0.09952   1.159  0.24635    
LexiconType1                 0.05799    0.11335   0.512  0.60896    
ot1:GroupType1               0.18242    0.14233   1.282  0.19995    
ot2:GroupType1              -0.03445    0.14245  -0.242  0.80890    
ot3:GroupType1              -0.10020    0.14278  -0.702  0.48279    
ot1:LexiconType1            -0.39684    0.20914  -1.897  0.05777 .  
ot2:LexiconType1             0.16562    0.19950   0.830  0.40643    
ot3:LexiconType1             0.05031    0.17101   0.294  0.76860    
GroupType1:LexiconType1      0.06120    0.09923   0.617  0.53743    
ot1:GroupType1:LexiconType1  0.03357    0.14228   0.236  0.81347    
ot2:GroupType1:LexiconType1 -0.06016    0.14222  -0.423  0.67230    
ot3:GroupType1:LexiconType1  0.06582    0.14275   0.461  0.64475    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


Correlation matrix not shown by default, as p = 16 > 12.
Use print(x, correlation=TRUE)  or
    vcov(x)        if you need it

summary(ADJ_second_order)

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
 Family: binomial  ( logit )
Formula: Adj_B ~ (ot1 + ot2) * GroupType * LexiconType + (1 | Unique_Participant) +      (1 | Meaning)
   Data: df_director
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

     AIC      BIC   logLik deviance df.resid 
  4210.8   4299.9  -2091.4   4182.8     4279 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.5876 -0.5289 -0.3728 -0.2181  5.3221 

Random effects:
 Groups             Name        Variance Std.Dev.
 Unique_Participant (Intercept) 0.9814   0.9907  
 Meaning            (Intercept) 0.2225   0.4717  
Number of obs: 4293, groups:  Unique_Participant, 156; Meaning, 18

Fixed effects:
                             Estimate Std. Error z value Pr(>|z|)    
(Intercept)                 -1.544456   0.158929  -9.718   <2e-16 ***
ot1                          0.538404   0.183244   2.938   0.0033 ** 
ot2                          0.001363   0.175009   0.008   0.9938    
GroupType1                   0.137403   0.094201   1.459   0.1447    
LexiconType1                 0.064454   0.111876   0.576   0.5645    
ot1:GroupType1               0.188905   0.140467   1.345   0.1787    
ot2:GroupType1              -0.045908   0.140628  -0.326   0.7441    
ot1:LexiconType1            -0.401966   0.183227  -2.194   0.0282 *  
ot2:LexiconType1             0.172121   0.174883   0.984   0.3250    
GroupType1:LexiconType1      0.055042   0.094117   0.585   0.5587    
ot1:GroupType1:LexiconType1  0.036263   0.140436   0.258   0.7962    
ot2:GroupType1:LexiconType1 -0.058186   0.140438  -0.414   0.6786    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) ot1    ot2    GrpTy1 LxcnT1 ot1:GT1 ot2:GT1 o1:LT1 o2:LT1 GT1:LT o1:GT1:
ot1         -0.101                                                                         
ot2          0.079 -0.386                                                                  
GroupType1  -0.347 -0.006 -0.001                                                           
LexiconTyp1 -0.049  0.020 -0.006  0.002                                                    
ot1:GrpTyp1 -0.190  0.255  0.133  0.385 -0.002                                             
ot2:GrpTyp1  0.164  0.123  0.303 -0.369  0.011 -0.007                                      
ot1:LxcnTy1  0.015 -0.111  0.050  0.018 -0.143 -0.015   0.004                              
ot2:LxcnTy1 -0.005  0.049 -0.087 -0.013  0.116  0.001  -0.023  -0.386                      
GrpTyp1:LT1  0.004  0.019 -0.014 -0.063 -0.498  0.032  -0.024  -0.007  0.001               
ot1:GT1:LT1 -0.001 -0.015  0.001  0.031 -0.271 -0.129   0.041   0.254  0.134  0.385        
ot2:GT1:LT1  0.007  0.004 -0.023 -0.022  0.239  0.041  -0.112   0.124  0.301 -0.367 -0.006

summary(ADJ_first_order)

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
 Family: binomial  ( logit )
Formula: Adj_B ~ ot1 * GroupType * LexiconType + (1 | Unique_Participant) +      (1 | Meaning)
   Data: df_director
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

     AIC      BIC   logLik deviance df.resid 
  4204.5   4268.1  -2092.2   4184.5     4283 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.5938 -0.5306 -0.3726 -0.2207  5.5188 

Random effects:
 Groups             Name        Variance Std.Dev.
 Unique_Participant (Intercept) 0.9936   0.9968  
 Meaning            (Intercept) 0.2226   0.4718  
Number of obs: 4293, groups:  Unique_Participant, 156; Meaning, 18

Fixed effects:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                 -1.53490    0.15697  -9.778  < 2e-16 ***
ot1                          0.55442    0.16306   3.400 0.000674 ***
GroupType1                   0.12145    0.08699   1.396 0.162683    
LexiconType1                 0.07129    0.10900   0.654 0.513052    
ot1:GroupType1               0.18883    0.13927   1.356 0.175154    
ot1:LexiconType1            -0.29691    0.16308  -1.821 0.068658 .  
GroupType1:LexiconType1      0.02557    0.08697   0.294 0.768723    
ot1:GroupType1:LexiconType1  0.01512    0.13924   0.109 0.913533    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) ot1    GrpTy1 LxcnT1 ot1:GT1 o1:LT1 GT1:LT
ot1         -0.125                                           
GroupType1  -0.318  0.114                                    
LexiconTyp1 -0.058  0.012  0.023                             
ot1:GrpTyp1 -0.201  0.363  0.410 -0.011                      
ot1:LxcnTy1  0.006 -0.104  0.027 -0.177 -0.035               
GrpTyp1:LT1  0.020  0.028 -0.108 -0.464  0.052   0.113       
ot1:GT1:LT1 -0.006 -0.035  0.051 -0.292 -0.122   0.363  0.410

5.4 Syllable repetition: Model comparison

ADJ_model.names <- c("ADJ first order","ADJ second order","ADJ third order")
ADJ_summ.table <- do.call(rbind, lapply(list(ADJ_first_order,ADJ_second_order,ADJ_third_order), broom::glance))
ADJ_table.cols <- c("df.residual", "deviance", "AIC")
ADJ_reported.table <- ADJ_summ.table[ADJ_table.cols]
names(ADJ_reported.table) <- c("Resid.Df", "Resid.Dev", "AIC")
ADJ_reported.table[['dAIC']] <-  with(ADJ_reported.table, AIC - min(AIC))
ADJ_reported.table[['AIC_weight']] <- with(ADJ_reported.table, exp(- 0.5 * dAIC) / sum(exp(- 0.5 * dAIC)))
ADJ_reported.table$AIC <- NULL
ADJ_reported.table$AIC_weight <- round(ADJ_reported.table$AIC_weight, 2)
ADJ_reported.table$dAIC <- round(ADJ_reported.table$dAIC, 1)
ADJ_reported.table$Resid.Dev <- round(ADJ_reported.table$Resid.Dev, 2)
row.names(ADJ_reported.table) <- ADJ_model.names

Setting row names on a tibble is deprecated.

View(ADJ_reported.table)
sjPlot::tab_df(ADJ_reported.table,
               file="model comparison _ADJ.doc")
sjPlot::tab_df(round(coef(summary(ADJ_first_order)),3),file='ADJ_fixed_effects.doc')

5.5 Syllable repetition: Plots showing model fit of 1st and 2nd order models by group type

df_director_adj_plots <- subset(df_director, !(is.na(df_director$Adj_B)))
  
ggplot(df_director_adj_plots, aes(Generation, Adj_B, shape=GroupType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(ADJ_first_order), linetype=GroupType),
               fun.y=mean, geom="line") +
  ylab("Adjacent repetition")+ 
  ggtitle('Adjacent repetition by group type - first order model')

dev.print(pdf,'Adjacent repetition by group type - first order model.pdf')

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ggplot(df_director_adj_plots, aes(Generation, Adj_B, color=GroupType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(ADJ_second_order), linetype=GroupType),
               fun.y=mean, geom="line") +
  ylab("Adjacent Repetition") +
  ggtitle('Adjacent repetition by group type - second order model')

dev.print(pdf,'Adjacent repetition by group type - second order model.pdf')

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5.6 Syllable repetition: Plots showing model fit of 1st and 2nd order models by lexicon type

ggplot(df_director_adj_plots, aes(Generation, Adj_B, color=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(ADJ_first_order), linetype=LexiconType),
               fun.y=mean, geom="line") +
  ylab("Adjacent repetition")+ 
  ggtitle('Adjacent repetition by lexicon type - first order model')

dev.print(pdf,'Adjacent repetition by lexicon type - first order model.pdf')

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ggplot(df_director_adj_plots, aes(Generation, Adj_B, color=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(ADJ_second_order), linetype=LexiconType),
               fun.y=mean, geom="line") +
  ylab("Adjacent repetition")+ 
  ggtitle('Adjacent repetition by lexicon type - second order model')

dev.print(pdf,'Adjacent repetition by lexicon type - second order model.pdf')

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6 Accuracy

6.1 Descriptive plot

groups2 = ddply(df_matcher, c("GroupType", "LexiconType", "GroupNumber"), summarise, n=length(Block))
groups2$group_id = factor(LETTERS[1:24])
df_matcher_plot = merge(df_matcher, groups, all.x = TRUE)
ggplot(df_matcher_plot, aes(Generation, Score, group = group_id, colour=GroupType, linetype=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="line") +
  ylab("Accuracy")+ 
  ggtitle('Accuracy group type and lexicon size')

dev.print(pdf, 'Accuracy by group type - dog dinner.pdf')

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6.2 Accuracy: Full models

#ACC_first_order <- glmer(Score ~ #maximal random effect structure, up to linear term 
#                        ot1 * GroupType * LexiconType  + (1+ot1|Unique_Participant) + 
#                       (1+ot1 |Meaning) + (1+ot1|Pair), data=df_matcher, family=binomial, 
#                       control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
#ACC_second_order <- glmer(Score ~ #maximal random effect structure, up to quadratic term 
#                     (ot1+ot2) * GroupType * LexiconType + (1+ot1+ot2|Unique_Participant) + 
#                     (1+ot1+ot2 |Meaning) + (1+ot1+ot2|Pair), data=df_matcher, family=binomial, 
#                     control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
#ACC_third_order <- glmer(Score ~ #maximal random effect structure, up to cubic term 
#                     (ot1+ot2+ot3) * GroupType * LexiconType + (1+ot1+ot2+ot3|Unique_Participant) + 
#                   (1+ot1+ot2+ot3 |Meaning) + (1+ot1+ot2+ot3|Pair), data=df_matcher, family=binomial, 
#                   control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))

6.3 Accuracy: Reduced models

The full models above also result in singular fit. So they are reduced below.

ACC_first_order <- glmer(Score ~ #maximal random effect structure, up to linear term 
                         ot1 * GroupType * LexiconType + (1|Unique_Participant) + (1|Meaning),
                       data=df_matcher, family=binomial, 
                       control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
ACC_second_order <- glmer(Score ~ #maximal random effect structure, up to quadratic term 
                      (ot1+ot2) * GroupType * LexiconType + (1|Unique_Participant) + (1|Meaning),
                    data=df_matcher, family=binomial, 
                    control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
ACC_third_order <- glmer(Score ~ #maximal random effect structure, up to cubic term 
                      (ot1+ot2+ot3) * GroupType * LexiconType + (1|Unique_Participant) + (1|Meaning),
                    data=df_matcher, family=binomial, 
                    control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
summary(ACC_third_order)

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
 Family: binomial  ( logit )
Formula: Score ~ (ot1 + ot2 + ot3) * GroupType * LexiconType + (1 | Unique_Participant) +      (1 | Meaning)
   Data: df_matcher
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

     AIC      BIC   logLik deviance df.resid 
  4070.5   4181.9  -2017.3   4034.5     3575 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-6.1190 -0.9143  0.4321  0.6505  1.5580 

Random effects:
 Groups             Name        Variance Std.Dev.
 Unique_Participant (Intercept) 0.55055  0.7420  
 Meaning            (Intercept) 0.03745  0.1935  
Number of obs: 3593, groups:  Unique_Participant, 144; Meaning, 18

Fixed effects:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                  1.10906    0.10614  10.449  < 2e-16 ***
ot1                          0.88270    0.12588   7.012 2.35e-12 ***
ot2                         -0.09864    0.12379  -0.797 0.425558    
ot3                          0.10352    0.12181   0.850 0.395405    
GroupType1                  -0.31792    0.09418  -3.376 0.000736 ***
LexiconType1                -0.32208    0.09513  -3.386 0.000710 ***
ot1:GroupType1              -0.28119    0.12573  -2.236 0.025322 *  
ot2:GroupType1              -0.11101    0.12379  -0.897 0.369854    
ot3:GroupType1              -0.04318    0.12180  -0.355 0.722946    
ot1:LexiconType1            -0.31164    0.12576  -2.478 0.013209 *  
ot2:LexiconType1            -0.07789    0.12379  -0.629 0.529213    
ot3:LexiconType1             0.06388    0.12182   0.524 0.600049    
GroupType1:LexiconType1      0.08920    0.09413   0.948 0.343319    
ot1:GroupType1:LexiconType1  0.02448    0.12572   0.195 0.845639    
ot2:GroupType1:LexiconType1  0.12381    0.12379   1.000 0.317238    
ot3:GroupType1:LexiconType1  0.16946    0.12183   1.391 0.164259    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


Correlation matrix not shown by default, as p = 16 > 12.
Use print(x, correlation=TRUE)  or
    vcov(x)        if you need it

summary(ACC_second_order)

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
 Family: binomial  ( logit )
Formula: Score ~ (ot1 + ot2) * GroupType * LexiconType + (1 | Unique_Participant) +      (1 | Meaning)
   Data: df_matcher
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

     AIC      BIC   logLik deviance df.resid 
  4065.3   4151.9  -2018.7   4037.3     3579 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-5.6764 -0.9113  0.4319  0.6498  1.6065 

Random effects:
 Groups             Name        Variance Std.Dev.
 Unique_Participant (Intercept) 0.55588  0.7456  
 Meaning            (Intercept) 0.03741  0.1934  
Number of obs: 3593, groups:  Unique_Participant, 144; Meaning, 18

Fixed effects:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                  1.10628    0.10634  10.403  < 2e-16 ***
ot1                          0.87276    0.12473   6.997 2.61e-12 ***
ot2                         -0.11617    0.12191  -0.953 0.340617    
GroupType1                  -0.31668    0.09441  -3.354 0.000796 ***
LexiconType1                -0.32036    0.09537  -3.359 0.000781 ***
ot1:GroupType1              -0.27350    0.12458  -2.195 0.028134 *  
ot2:GroupType1              -0.08912    0.12190  -0.731 0.464743    
ot1:LexiconType1            -0.30254    0.12461  -2.428 0.015183 *  
ot2:LexiconType1            -0.06219    0.12190  -0.510 0.609955    
GroupType1:LexiconType1      0.08662    0.09437   0.918 0.358681    
ot1:GroupType1:LexiconType1  0.01577    0.12457   0.127 0.899269    
ot2:GroupType1:LexiconType1  0.10388    0.12190   0.852 0.394120    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) ot1    ot2    GrpTy1 LxcnT1 ot1:GT1 ot2:GT1 o1:LT1 o2:LT1 GT1:LT o1:GT1:
ot1          0.080                                                                         
ot2          0.015  0.112                                                                  
GroupType1  -0.499 -0.055 -0.021                                                           
LexiconTyp1 -0.093 -0.067 -0.017  0.034                                                    
ot1:GrpTyp1 -0.049  0.245 -0.072  0.084  0.041                                             
ot2:GrpTyp1 -0.019 -0.072  0.296  0.018  0.023  0.113                                      
ot1:LxcnTy1 -0.060 -0.234 -0.082  0.041  0.083  0.112   0.063                              
ot2:LxcnTy1 -0.015 -0.082 -0.202  0.024  0.018  0.063   0.079   0.112                      
GrpTyp1:LT1  0.031  0.042  0.023 -0.079 -0.559 -0.065  -0.018  -0.056 -0.021               
ot1:GT1:LT1  0.035  0.111  0.063 -0.065 -0.055 -0.234  -0.082   0.245 -0.072  0.083        
ot2:GT1:LT1  0.022  0.063  0.079 -0.018 -0.021 -0.082  -0.202  -0.072  0.296  0.019  0.112

summary(ACC_first_order)

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
 Family: binomial  ( logit )
Formula: Score ~ ot1 * GroupType * LexiconType + (1 | Unique_Participant) +      (1 | Meaning)
   Data: df_matcher
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

     AIC      BIC   logLik deviance df.resid 
  4060.2   4122.1  -2020.1   4040.2     3583 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-5.3773 -0.9108  0.4328  0.6502  1.6203 

Random effects:
 Groups             Name        Variance Std.Dev.
 Unique_Participant (Intercept) 0.56201  0.7497  
 Meaning            (Intercept) 0.03734  0.1932  
Number of obs: 3593, groups:  Unique_Participant, 144; Meaning, 18

Fixed effects:
                             Estimate Std. Error z value Pr(>|z|)    
(Intercept)                  1.105108   0.106587  10.368  < 2e-16 ***
ot1                          0.870556   0.123001   7.078 1.47e-12 ***
GroupType1                  -0.314923   0.094714  -3.325 0.000884 ***
LexiconType1                -0.318162   0.095662  -3.326 0.000881 ***
ot1:GroupType1              -0.263141   0.122847  -2.142 0.032192 *  
ot1:LexiconType1            -0.288397   0.122878  -2.347 0.018924 *  
GroupType1:LexiconType1      0.084818   0.094668   0.896 0.370279    
ot1:GroupType1:LexiconType1 -0.003059   0.122841  -0.025 0.980136    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) ot1    GrpTy1 LxcnT1 ot1:GT1 o1:LT1 GT1:LT
ot1          0.074                                           
GroupType1  -0.500 -0.046                                    
LexiconTyp1 -0.090 -0.058  0.031                             
ot1:GrpTyp1 -0.041  0.293  0.077  0.030                      
ot1:LxcnTy1 -0.052 -0.204  0.030  0.077  0.075               
GrpTyp1:LT1  0.029  0.031 -0.077 -0.559 -0.056  -0.047       
ot1:GT1:LT1  0.026  0.074 -0.056 -0.046 -0.203   0.293  0.076

6.4 Accuracy: Model comparison

ACC_model.names <- c("ACC first order","ACC second order","ACC third order")
ACC_summ.table <- do.call(rbind, lapply(list(ACC_first_order,ACC_second_order,ACC_third_order), broom::glance))
ACC_table.cols <- c("df.residual", "deviance", "AIC")
ACC_reported.table <- ACC_summ.table[ACC_table.cols]
names(ACC_reported.table) <- c("Resid.Df", "Resid.Dev", "AIC")
ACC_reported.table[['dAIC']] <-  with(ACC_reported.table, AIC - min(AIC))
ACC_reported.table[['AIC_weight']] <- with(ACC_reported.table, exp(- 0.5 * dAIC) / sum(exp(- 0.5 * dAIC)))
ACC_reported.table$AIC <- NULL
ACC_reported.table$dAIC <- round(ACC_reported.table$dAIC, 1)
ACC_reported.table$AIC_weight <- round(ACC_reported.table$AIC_weight, 2)
ACC_reported.table$Resid.Dev <- round(ACC_reported.table$Resid.Dev, 2)
row.names(ACC_reported.table) <- ACC_model.names

Setting row names on a tibble is deprecated.

View(ACC_reported.table)
sjPlot::tab_df(ACC_reported.table,
               file="model comparison _ACC.doc")

6.5 Accuracy: Plots showing model fit of 1st and 2nd order models by group type

ggplot(df_matcher, aes(Generation, Score, shape=GroupType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(ACC_first_order), linetype=GroupType),
               fun.y=mean, geom="line") +
  ylab("Accuracy")+ 
  ggtitle('Accuracy by group type - first order model')

dev.print(pdf,'Accuracy by group type - first order model.pdf')

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ggplot(df_matcher, aes(Generation, Score, color=GroupType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(ACC_second_order), linetype=GroupType),
               fun.y=mean, geom="line") +
  ylab("Accuracy")+ 
  ggtitle('Accuracy by group type - second order model')

dev.print(pdf,'Accuracy by group type - second order model.pdf')

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6.6 Accuracy: Plots showing model fit of 1st and 2nd order models by lexicon type

ggplot(df_matcher, aes(Generation, Score, shape=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(ACC_first_order), linetype=LexiconType),
               fun.y=mean, geom="line") +
  ylab("Accuracy")+ 
  ggtitle('Accuracy by lexicon type - first order model')

dev.print(pdf,'Accuracy by lexicon type - first order model.pdf')

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ggplot(df_matcher, aes(Generation, Score, shape=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(ACC_second_order), linetype=LexiconType),
               fun.y=mean, geom="line") +
  ylab("Accuracy")+ 
  ggtitle('Accuracy by lexicon type - second order model')

dev.print(pdf,'Accuracy by lexicon type - second order model.pdf')

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sjPlot::tab_df(round(coef(summary(ACC_first_order)),3),file='ACC_fixed_effects.doc')

7 Homonymy

7.1 Coding homonymy

These are based on iteration 2 only. Homonymy is calculated as proportions.

df_homonymy<-setNames(aggregate(df_director_generations_1_to_5$New_Redup_B, by=list(df_director_generations_1_to_5$GroupType,df_director_generations_1_to_5$LexiconType,df_director_generations_1_to_5$GroupNumber,df_director_generations_1_to_5$Generation,df_director_generations_1_to_5$Unique_language,df_director_generations_1_to_5$Pair, df_director_generations_1_to_5$Unique_Participant), FUN=mean),c('GroupType','LexiconType','GroupNumber','Generation','Unique_language','Pair', 'Unique_Participant','New_Redup_B_mean'))
df_homonymy$Word_repetition<-NA
for(i in unique(df_homonymy$Unique_language)){
  subs_ <- subset(df_director_generations_1_to_5,Unique_language==i)
  for(j in 1:nrow(df_homonymy)){
    if(toString(df_homonymy[j,]$Unique_language) == i){
      df_homonymy[j,]$Word_repetition <- (length(subs_$TypedLabel)-length(unique(subs_$TypedLabel)))/length(subs_$TypedLabel)
      
    }
  }
}
initial_languages<-subset(df_director,Generation==0)
df_initial_languages <-setNames(aggregate(initial_languages$New_Redup_B, by=list(initial_languages$GroupType,initial_languages$LexiconType,initial_languages$GroupNumber,initial_languages$Generation,initial_languages$Unique_language,initial_languages$Pair, initial_languages$Unique_Participant), FUN=mean),c('GroupType','LexiconType','GroupNumber','Generation','Unique_language','Pair', 'Unique_Participant','New_Redup_B_mean'))
df_initial_languages$Word_repetition <-0
df_homonymy<-rbind(df_initial_languages,df_homonymy)
df_homonymy_1_to_5 <- subset(df_homonymy, Generation>0) #generations 1 to 5
cor(df_homonymy_1_to_5$New_Redup_B_mean,df_homonymy_1_to_5$Word_repetition) #looking at mean CVC redup. When looking at sum CVC redup the correlation was .20...

[1] 0.176133

df_homonymy$timebin = df_homonymy$Generation + 1
t_ = poly(unique(df_homonymy$timebin), 3)
df_homonymy[,paste("ot", 1:3, sep="")] <- t_[df_homonymy$timebin, 1:3]
contrasts(df_homonymy$LexiconType) = contr.sum(2)
contrasts(df_homonymy$GroupType) = contr.sum(2)

7.2 Homonymy: Models

Can’t fit a 3rd order model because there are fewer observations than random effects

homonymy_first_order <- lmer(Word_repetition ~
                          ot1*GroupType*LexiconType +
                          (1|Unique_Participant),
                        data=df_homonymy, control=lmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=2e5)))
homonymy_second_order <- lmer(Word_repetition ~
                          (ot1+ot2)*GroupType*LexiconType +
                            (1|Unique_Participant),
                        data=df_homonymy, control=lmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=2e5)))
summary(homonymy_first_order)

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Word_repetition ~ ot1 * GroupType * LexiconType + (1 | Unique_Participant)
   Data: df_homonymy
Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))

REML criterion at convergence: -718.7

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.66121 -0.35552 -0.06661  0.27156  2.99173 

Random effects:
 Groups             Name        Variance Std.Dev.
 Unique_Participant (Intercept) 0.006034 0.07768 
 Residual                       0.001311 0.03621 
Number of obs: 288, groups:  Unique_Participant, 156

Fixed effects:
                              Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)                  6.252e-02  7.815e-03  1.326e+02   8.000 5.47e-13 ***
ot1                          3.912e-02  1.036e-02  2.659e+02   3.776 0.000196 ***
GroupType1                   3.999e-02  5.768e-03  2.219e+02   6.933 4.44e-11 ***
LexiconType1                 3.035e-03  7.815e-03  1.326e+02   0.388 0.698384    
ot1:GroupType1               6.701e-02  9.056e-03  2.242e+02   7.400 2.72e-12 ***
ot1:LexiconType1             3.169e-02  1.036e-02  2.659e+02   3.059 0.002445 ** 
GroupType1:LexiconType1      3.654e-04  5.768e-03  2.219e+02   0.063 0.949542    
ot1:GroupType1:LexiconType1 -1.794e-03  9.056e-03  2.242e+02  -0.198 0.843179    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) ot1    GrpTy1 LxcnT1 ot1:GT1 o1:LT1 GT1:LT
ot1         -0.254                                           
GroupType1  -0.494  0.321                                    
LexiconTyp1  0.000  0.000  0.000                             
ot1:GrpTyp1 -0.384  0.542  0.628  0.000                      
ot1:LxcnTy1  0.000  0.000  0.000 -0.254  0.000               
GrpTyp1:LT1  0.000  0.000  0.000 -0.494  0.000   0.321       
ot1:GT1:LT1  0.000  0.000  0.000 -0.384  0.000   0.542  0.628

summary(homonymy_second_order)

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Word_repetition ~ (ot1 + ot2) * GroupType * LexiconType + (1 |      Unique_Participant)
   Data: df_homonymy
Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))

REML criterion at convergence: -689.3

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.59096 -0.35469 -0.04545  0.26614  2.92943 

Random effects:
 Groups             Name        Variance Std.Dev.
 Unique_Participant (Intercept) 0.006162 0.07850 
 Residual                       0.001319 0.03632 
Number of obs: 288, groups:  Unique_Participant, 156

Fixed effects:
                              Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)                  6.186e-02  8.292e-03  1.180e+02   7.459 1.61e-11 ***
ot1                          3.810e-02  1.172e-02  2.670e+02   3.252  0.00129 ** 
ot2                         -4.644e-04  1.139e-02  2.617e+02  -0.041  0.96750    
GroupType1                   4.095e-02  6.669e-03  1.641e+02   6.140 6.00e-09 ***
LexiconType1                 2.533e-03  8.292e-03  1.180e+02   0.306  0.76052    
ot1:GroupType1               6.697e-02  9.236e-03  2.191e+02   7.252 7.00e-12 ***
ot2:GroupType1              -2.572e-03  9.293e-03  2.162e+02  -0.277  0.78224    
ot1:LexiconType1             3.407e-02  1.172e-02  2.670e+02   2.907  0.00395 ** 
ot2:LexiconType1            -5.874e-03  1.139e-02  2.617e+02  -0.516  0.60647    
GroupType1:LexiconType1      4.063e-04  6.669e-03  1.641e+02   0.061  0.95149    
ot1:GroupType1:LexiconType1 -2.523e-03  9.236e-03  2.191e+02  -0.273  0.78500    
ot2:GroupType1:LexiconType1 -1.263e-03  9.293e-03  2.162e+02  -0.136  0.89204    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) ot1    ot2    GrpTy1 LxcnT1 ot1:GT1 ot2:GT1 o1:LT1 o2:LT1 GT1:LT o1:GT1:
ot1         -0.189                                                                         
ot2          0.178 -0.312                                                                  
GroupType1  -0.551  0.135 -0.127                                                           
LexiconTyp1  0.000  0.000  0.000  0.000                                                    
ot1:GrpTyp1 -0.336  0.432  0.152  0.526  0.000                                             
ot2:GrpTyp1  0.305  0.146  0.469 -0.477  0.000  0.068                                      
ot1:LxcnTy1  0.000  0.000  0.000  0.000 -0.189  0.000   0.000                              
ot2:LxcnTy1  0.000  0.000  0.000  0.000  0.178  0.000   0.000  -0.312                      
GrpTyp1:LT1  0.000  0.000  0.000  0.000 -0.551  0.000   0.000   0.135 -0.127               
ot1:GT1:LT1  0.000  0.000  0.000  0.000 -0.336  0.000   0.000   0.432  0.152  0.526        
ot2:GT1:LT1  0.000  0.000  0.000  0.000  0.305  0.000   0.000   0.146  0.469 -0.477  0.068

7.3 Homonymy: Model comparison

homonyms_model.names <- c("Homonymy first order","Homonymy second order")
homonyms_summ.table <- do.call(rbind, lapply(list(homonymy_first_order,homonymy_second_order), broom::glance))
homonyms_table.cols <- c("df.residual", "deviance", "AIC")
homonyms_reported.table <- homonyms_summ.table[homonyms_table.cols]
names(homonyms_reported.table) <- c("Resid. Df", "Resid.Dev", "AIC")
homonyms_reported.table[['dAIC']] <-  with(homonyms_reported.table, AIC - min(AIC))
homonyms_reported.table[['AIC_weight']] <- with(homonyms_reported.table, exp(- 0.5 * dAIC) / sum(exp(- 0.5 * dAIC)))
homonyms_reported.table$AIC <- NULL
homonyms_reported.table$AIC_weight <- round(homonyms_reported.table$AIC_weight, 2)
homonyms_reported.table$dAIC <- round(homonyms_reported.table$dAIC, 1)
homonyms_reported.table$Resid.Dev <- round(homonyms_reported.table$Resid.Dev, 2)
row.names(homonyms_reported.table) <- homonyms_model.names

Setting row names on a tibble is deprecated.

View(homonyms_reported.table)
anova(homonymy_first_order,homonymy_second_order)

refitting model(s) with ML (instead of REML)

Data: df_homonymy
Models:
homonymy_first_order: Word_repetition ~ ot1 * GroupType * LexiconType + (1 | Unique_Participant)
homonymy_second_order: Word_repetition ~ (ot1 + ot2) * GroupType * LexiconType + (1 | 
homonymy_second_order:     Unique_Participant)
                      Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)
homonymy_first_order  10 -763.57 -726.94 391.79  -783.57                         
homonymy_second_order 14 -755.95 -704.67 391.97  -783.95 0.3756      4     0.9844

sjPlot::tab_df(homonyms_reported.table,
               file="model comparison homonyms_.doc")
sjPlot::tab_df(round(coef(summary(homonymy_first_order)),3),file='homonyms_fixed_effects.doc')

7.4 Homonymy: Plots showing model fit of 1st and 2nd order models by group type

ggplot(df_homonymy, aes(Generation, Word_repetition, shape=GroupType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(homonymy_first_order), linetype=GroupType),
               fun.y=mean, geom="line") +
  ylab("Proportion of homonymy")

dev.print(pdf,'Homonymy by group type - first order model.pdf')

quartz_off_screen 
                2

ggplot(df_homonymy, aes(Generation, Word_repetition, color=GroupType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(homonymy_second_order), linetype=GroupType),
               fun.y=mean, geom="line") +
  ylab("Proportion of homonymy")

dev.print(pdf,'Homonymy by group type - second order model.pdf')

quartz_off_screen 
                2

7.5 Homonymy: Plots showing model fit of 1st and 2nd order models by lexicon type

ggplot(df_homonymy, aes(Generation, Word_repetition, shape=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(homonymy_first_order), linetype=LexiconType),
               fun.y=mean, geom="line") +
  ylab("Proportion of homonymy")

dev.print(pdf,'Homonymy by lexicon type - first order model.pdf')

quartz_off_screen 
                2

ggplot(df_homonymy, aes(Generation, Word_repetition, color=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(homonymy_second_order), linetype=LexiconType),
               fun.y=mean, geom="line") +
  ylab("Proportion of homonymy")

dev.print(pdf,'Homonymy by lexicon type - second order model.pdf')

quartz_off_screen 
                2

8 Homonymy: Logistic analysis

Analyze homonymy again, using 0/1 coding for word repetition and binomial stats

8.1 Homonymy (log): Models

df_homonymy$Word_repetition_binary = ifelse(df_homonymy$Word_repetition > 0, 1, 0)
homonymy_first_order_log <- glmer(Word_repetition_binary ~
                               ot1*GroupType*LexiconType + (1|Unique_Participant),
                             data=df_homonymy, family=binomial, 
                             control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
homonymy_second_order_log <- glmer(Word_repetition_binary ~
                                    (ot1+ot2)*GroupType*LexiconType + (1|Unique_Participant),
                                  data=df_homonymy, family=binomial, 
                                  control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
homonymy_third_order_log <- glmer(Word_repetition_binary ~
                                     (ot1+ot2+ot3)*GroupType*LexiconType + (1|Unique_Participant),
                                   data=df_homonymy, family=binomial, 
                                  control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
summary(homonymy_first_order_log)

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
 Family: binomial  ( logit )
Formula: Word_repetition_binary ~ ot1 * GroupType * LexiconType + (1 |      Unique_Participant)
   Data: df_homonymy
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

     AIC      BIC   logLik deviance df.resid 
   305.7    338.7   -143.9    287.7      279 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3399 -0.4370 -0.1921  0.3336  2.0135 

Random effects:
 Groups             Name        Variance Std.Dev.
 Unique_Participant (Intercept) 3.286    1.813   
Number of obs: 288, groups:  Unique_Participant, 156

Fixed effects:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                 -0.16379    0.26493  -0.618 0.536432    
ot1                          1.69728    0.57580   2.948 0.003201 ** 
GroupType1                   1.58281    0.43196   3.664 0.000248 ***
LexiconType1                 0.23858    0.28474   0.838 0.402103    
ot1:GroupType1               2.67830    0.77586   3.452 0.000556 ***
ot1:LexiconType1             0.27372    0.58093   0.471 0.637519    
GroupType1:LexiconType1     -0.03297    0.25705  -0.128 0.897942    
ot1:GroupType1:LexiconType1 -1.36152    0.59300  -2.296 0.021676 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) ot1    GrpTy1 LxcnT1 ot1:GT1 o1:LT1 GT1:LT
ot1          0.048                                           
GroupType1  -0.121  0.081                                    
LexiconTyp1 -0.007 -0.049  0.287                             
ot1:GrpTyp1  0.008  0.230  0.670  0.307                      
ot1:LxcnTy1 -0.073 -0.163  0.154  0.096  0.063               
GrpTyp1:LT1 -0.029  0.044 -0.157 -0.305 -0.182   0.014       
ot1:GT1:LT1  0.033 -0.093 -0.411 -0.195 -0.438   0.164  0.248

summary(homonymy_second_order_log)

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
 Family: binomial  ( logit )
Formula: Word_repetition_binary ~ (ot1 + ot2) * GroupType * LexiconType +      (1 | Unique_Participant)
   Data: df_homonymy
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

     AIC      BIC   logLik deviance df.resid 
   298.8    346.5   -136.4    272.8      275 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.4415 -0.4328 -0.1880  0.3536  3.5588 

Random effects:
 Groups             Name        Variance Std.Dev.
 Unique_Participant (Intercept) 1.361    1.167   
Number of obs: 288, groups:  Unique_Participant, 156

Fixed effects:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                 -0.31709    0.22011  -1.441 0.149696    
ot1                          1.83969    0.53858   3.416 0.000636 ***
ot2                         -1.84158    0.52716  -3.493 0.000477 ***
GroupType1                   1.26953    0.28193   4.503  6.7e-06 ***
LexiconType1                 0.20317    0.22319   0.910 0.362680    
ot1:GroupType1               2.18125    0.60706   3.593 0.000327 ***
ot2:GroupType1              -0.14429    0.49813  -0.290 0.772074    
ot1:LexiconType1             0.35009    0.52733   0.664 0.506759    
ot2:LexiconType1             0.09551    0.51961   0.184 0.854155    
GroupType1:LexiconType1      0.01316    0.21285   0.062 0.950700    
ot1:GroupType1:LexiconType1 -1.30560    0.52323  -2.495 0.012585 *  
ot2:GroupType1:LexiconType1 -0.68910    0.49401  -1.395 0.163042    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) ot1    ot2    GrpTy1 LxcnT1 ot1:GT1 ot2:GT1 o1:LT1 o2:LT1 GT1:LT o1:GT1:
ot1          0.010                                                                         
ot2          0.265 -0.162                                                                  
GroupType1  -0.183  0.098 -0.139                                                           
LexiconTyp1 -0.112 -0.098 -0.184  0.132                                                    
ot1:GrpTyp1 -0.108  0.132 -0.173  0.437  0.215                                             
ot2:GrpTyp1 -0.007 -0.125  0.083  0.023 -0.048 -0.130                                      
ot1:LxcnTy1 -0.146 -0.151 -0.149  0.136  0.038  0.031   0.135                              
ot2:LxcnTy1 -0.157 -0.139 -0.180 -0.016  0.251  0.131   0.036  -0.131                      
GrpTyp1:LT1  0.018  0.122 -0.014 -0.094 -0.177 -0.142  -0.154  -0.057 -0.038               
ot1:GT1:LT1  0.154 -0.059  0.198 -0.291 -0.124 -0.320  -0.094   0.001 -0.092  0.114        
ot2:GT1:LT1 -0.005  0.117  0.057 -0.204 -0.044 -0.195  -0.162  -0.097  0.054  0.191  0.000

summary(homonymy_third_order_log)

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
 Family: binomial  ( logit )
Formula: Word_repetition_binary ~ (ot1 + ot2 + ot3) * GroupType * LexiconType +      (1 | Unique_Participant)
   Data: df_homonymy
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

     AIC      BIC   logLik deviance df.resid 
   293.0    355.3   -129.5    259.0      271 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.7747 -0.4301 -0.1310  0.3791  2.0874 

Random effects:
 Groups             Name        Variance Std.Dev.
 Unique_Participant (Intercept) 1.301    1.141   
Number of obs: 288, groups:  Unique_Participant, 156

Fixed effects:
                             Estimate Std. Error z value Pr(>|z|)    
(Intercept)                 -0.440247   0.238064  -1.849 0.064417 .  
ot1                          2.573285   0.662650   3.883 0.000103 ***
ot2                         -2.181392   0.622676  -3.503 0.000460 ***
ot3                          1.592944   0.526002   3.028 0.002459 ** 
GroupType1                   1.232435   0.284710   4.329  1.5e-05 ***
LexiconType1                 0.169306   0.239656   0.706 0.479905    
ot1:GroupType1               1.920450   0.677763   2.834 0.004604 ** 
ot2:GroupType1              -0.116931   0.570672  -0.205 0.837649    
ot3:GroupType1              -0.227157   0.499726  -0.455 0.649423    
ot1:LexiconType1             0.183170   0.631328   0.290 0.771714    
ot2:LexiconType1            -0.041502   0.589329  -0.070 0.943857    
ot3:LexiconType1            -0.059721   0.513425  -0.116 0.907400    
GroupType1:LexiconType1     -0.005999   0.231297  -0.026 0.979308    
ot1:GroupType1:LexiconType1 -1.041604   0.619715  -1.681 0.092806 .  
ot2:GroupType1:LexiconType1 -0.657437   0.570436  -1.153 0.249109    
ot3:GroupType1:LexiconType1  0.787134   0.512633   1.535 0.124667    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


Correlation matrix not shown by default, as p = 16 > 12.
Use print(x, correlation=TRUE)  or
    vcov(x)        if you need it

numcols <- grep("^c\\.",names(df_homonymy))
dfs_homonymy <- df_homonymy
dfs_homonymy[,numcols] <- scale(dfs_homonymy[,numcols])
homonymy_first_order_log_sc <- update(homonymy_first_order_log, data=dfs_homonymy)
homonymy_second_order_log_sc <- update(homonymy_second_order_log, data=dfs_homonymy)

8.2 Homonymy (log): Model comparison

Model comparison shows that the second-order model has the best fit.

homonyms_log_model.names <- c("Homonymy first order log","Homonymy second order log", "Homonymy third order log")
homonyms_log_summ.table <- do.call(rbind, lapply(list(homonymy_first_order_log,homonymy_second_order, homonymy_third_order_log), broom::glance))
homonyms_log_table.cols <- c("df.residual", "deviance", "AIC")
homonyms_log_reported.table <- homonyms_log_summ.table[homonyms_log_table.cols]
names(homonyms_log_reported.table) <- c("Resid. Df", "Resid.Dev", "AIC")
homonyms_log_reported.table[['dAIC']] <-  with(homonyms_log_reported.table, AIC - min(AIC))
homonyms_log_reported.table[['AIC_weight']] <- with(homonyms_log_reported.table, exp(- 0.5 * dAIC) / sum(exp(- 0.5 * dAIC)))
homonyms_log_reported.table$AIC <- NULL
homonyms_log_reported.table$AIC_weight <- round(homonyms_log_reported.table$AIC_weight, 2)
homonyms_log_reported.table$dAIC <- round(homonyms_log_reported.table$dAIC, 1)
homonyms_log_reported.table$Resid.Dev <- round(homonyms_log_reported.table$Resid.Dev, 2)
row.names(homonyms_log_reported.table) <- homonyms_log_model.names

Setting row names on a tibble is deprecated.

View(homonyms_log_reported.table)
anova(homonymy_first_order_log,homonymy_second_order_log, homonymy_third_order_log)

Data: df_homonymy
Models:
homonymy_first_order_log: Word_repetition_binary ~ ot1 * GroupType * LexiconType + (1 | 
homonymy_first_order_log:     Unique_Participant)
homonymy_second_order_log: Word_repetition_binary ~ (ot1 + ot2) * GroupType * LexiconType + 
homonymy_second_order_log:     (1 | Unique_Participant)
homonymy_third_order_log: Word_repetition_binary ~ (ot1 + ot2 + ot3) * GroupType * LexiconType + 
homonymy_third_order_log:     (1 | Unique_Participant)
                          Df    AIC    BIC  logLik deviance  Chisq Chi Df Pr(>Chisq)   
homonymy_first_order_log   9 305.75 338.72 -143.88   287.75                            
homonymy_second_order_log 13 298.84 346.46 -136.42   272.84 14.912      4   0.004887 **
homonymy_third_order_log  17 293.02 355.30 -129.51   259.02 13.813      4   0.007917 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

sjPlot::tab_df(homonyms_log_reported.table,
               file="model comparison homonyms log_.doc")
sjPlot::tab_df(round(coef(summary(homonymy_first_order_log)),3),file='homonyms_log_fixed_effects.doc')
homonyms_log_reported.table

[38;5;246m# A tibble: 3 x 4[39m
  `Resid. Df` Resid.Dev  dAIC AIC_weight
[38;5;250m*[39m       [3m[38;5;246m<int>[39m[23m     [3m[38;5;246m<dbl>[39m[23m [3m[38;5;246m<dbl>[39m[23m      [3m[38;5;246m<dbl>[39m[23m
[38;5;250m1[39m         279      168.  967.          0
[38;5;250m2[39m         274     -[31m784[39m[31m.[39m    0           1
[38;5;250m3[39m         271      193.  954.          0

8.3 Homonymy (log): Plot showing model fit of 2nd order models by group type x lexicon size

ggplot(df_homonymy, aes(Generation, Word_repetition_binary, shape=GroupType, linetype=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(homonymy_second_order_log), linetype=LexiconType),
               fun.y=mean, geom="line") +
  ylab("Proportion of homonymy")

dev.print(pdf,'Homonymy Log by Group and Lexicon type - first order model.pdf')

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                2

9 Homonymy (Gen1-5)

Because there’s discontinuity between Generation/Round 0 and 1, redo the analysis in ‘Homonymy’ without Generation/Round 0 (i.e., only Generations 1-5).

9.1 Homonymy (Gen1-5): Models

Can’t fit a 3rd order model because there are fewer observations than random effects

homonymy_first_order_1to5 <- lmer(Word_repetition ~
                               ot1*GroupType*LexiconType + (1|Unique_Participant),
                             data=df_homonymy[df_homonymy$Generation != 0, ], 
                             control=lmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
homonymy_second_order_1to5 <- lmer(Word_repetition ~
                                (ot1+ot2)*GroupType*LexiconType + (1|Unique_Participant),
                              data=df_homonymy[df_homonymy$Generation != 0, ], 
                              control=lmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
summary(homonymy_first_order_1to5)

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Word_repetition ~ ot1 * GroupType * LexiconType + (1 | Unique_Participant)
   Data: df_homonymy[df_homonymy$Generation != 0, ]
Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

REML criterion at convergence: -538.6

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.26218 -0.42304 -0.07362  0.38388  2.49506 

Random effects:
 Groups             Name        Variance Std.Dev.
 Unique_Participant (Intercept) 0.006011 0.07753 
 Residual                       0.001809 0.04253 
Number of obs: 240, groups:  Unique_Participant, 144

Fixed effects:
                              Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)                   0.069581   0.009229  96.125742   7.539 2.63e-11 ***
ot1                           0.030930   0.013249 197.310275   2.335 0.020568 *  
GroupType1                    0.036768   0.009229  96.125742   3.984 0.000132 ***
LexiconType1                 -0.001947   0.009229  96.125742  -0.211 0.833352    
ot1:GroupType1                0.061434   0.013249 197.310275   4.637 6.42e-06 ***
ot1:LexiconType1              0.030930   0.013249 197.310275   2.335 0.020568 *  
GroupType1:LexiconType1       0.006560   0.009229  96.125742   0.711 0.478956    
ot1:GroupType1:LexiconType1  -0.005383   0.013249 197.310275  -0.406 0.684960    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) ot1    GrpTy1 LxcnT1 ot1:GT1 o1:LT1 GT1:LT
ot1         -0.172                                           
GroupType1  -0.570 -0.107                                    
LexiconTyp1  0.000  0.000  0.000                             
ot1:GrpTyp1 -0.107  0.624 -0.172  0.000                      
ot1:LxcnTy1  0.000  0.000  0.000 -0.172  0.000               
GrpTyp1:LT1  0.000  0.000  0.000 -0.570  0.000  -0.107       
ot1:GT1:LT1  0.000  0.000  0.000 -0.107  0.000   0.624 -0.172

summary(homonymy_second_order_1to5)

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Word_repetition ~ (ot1 + ot2) * GroupType * LexiconType + (1 |      Unique_Participant)
   Data: df_homonymy[df_homonymy$Generation != 0, ]
Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

REML criterion at convergence: -513.8

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.08844 -0.46938 -0.09452  0.41061  2.37937 

Random effects:
 Groups             Name        Variance Std.Dev.
 Unique_Participant (Intercept) 0.006069 0.07790 
 Residual                       0.001827 0.04274 
Number of obs: 240, groups:  Unique_Participant, 144

Fixed effects:
                              Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)                   0.072678   0.009790 102.368191   7.423 3.49e-11 ***
ot1                           0.019823   0.017431 194.408408   1.137 0.256854    
ot2                           0.016224   0.016434 194.408408   0.987 0.324779    
GroupType1                    0.038033   0.009790 102.368191   3.885 0.000182 ***
LexiconType1                 -0.003479   0.009790 102.368191  -0.355 0.723060    
ot1:GroupType1                0.056897   0.017431 194.408408   3.264 0.001298 ** 
ot2:GroupType1                0.006626   0.016434 194.408408   0.403 0.687251    
ot1:LexiconType1              0.036423   0.017431 194.408408   2.090 0.037961 *  
ot2:LexiconType1             -0.008023   0.016434 194.408408  -0.488 0.625981    
GroupType1:LexiconType1       0.007246   0.009790 102.368191   0.740 0.460913    
ot1:GroupType1:LexiconType1  -0.007845   0.017431 194.408408  -0.450 0.653194    
ot2:GroupType1:LexiconType1   0.003595   0.016434 194.408408   0.219 0.827057    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) ot1    ot2    GrpTy1 LxcnT1 ot1:GT1 ot2:GT1 o1:LT1 o2:LT1 GT1:LT o1:GT1:
ot1         -0.331                                                                         
ot2          0.321 -0.645                                                                  
GroupType1  -0.447 -0.207  0.200                                                           
LexiconTyp1  0.000  0.000  0.000  0.000                                                    
ot1:GrpTyp1 -0.207  0.624 -0.403 -0.331  0.000                                             
ot2:GrpTyp1  0.200 -0.403  0.624  0.321  0.000 -0.645                                      
ot1:LxcnTy1  0.000  0.000  0.000  0.000 -0.331  0.000   0.000                              
ot2:LxcnTy1  0.000  0.000  0.000  0.000  0.321  0.000   0.000  -0.645                      
GrpTyp1:LT1  0.000  0.000  0.000  0.000 -0.447  0.000   0.000  -0.207  0.200               
ot1:GT1:LT1  0.000  0.000  0.000  0.000 -0.207  0.000   0.000   0.624 -0.403 -0.331        
ot2:GT1:LT1  0.000  0.000  0.000  0.000  0.200  0.000   0.000  -0.403  0.624  0.321 -0.645

9.2 Homonymy (Gen1-5): Model comparison

homonyms_1to5_model.names <- c("Homonymy first order 1 to 5","Homonymy second order 1 to 5")
homonyms_1to5_summ.table <- do.call(rbind, lapply(list(homonymy_first_order_1to5,homonymy_second_order_1to5), broom::glance))
homonyms_1to5_table.cols <- c("df.residual", "deviance", "AIC")
homonyms_1to5_reported.table <- homonyms_1to5_summ.table[homonyms_1to5_table.cols]
names(homonyms_1to5_reported.table) <- c("Resid. Df", "Resid.Dev", "AIC")
homonyms_1to5_reported.table[['dAIC']] <-  with(homonyms_1to5_reported.table, AIC - min(AIC))
homonyms_1to5_reported.table[['AIC_weight']] <- with(homonyms_1to5_reported.table, exp(- 0.5 * dAIC) / sum(exp(- 0.5 * dAIC)))
homonyms_1to5_reported.table$AIC <- NULL
homonyms_1to5_reported.table$AIC_weight <- round(homonyms_1to5_reported.table$AIC_weight, 2)
homonyms_1to5_reported.table$dAIC <- round(homonyms_1to5_reported.table$dAIC, 1)
homonyms_1to5_reported.table$Resid.Dev <- round(homonyms_1to5_reported.table$Resid.Dev, 2)
row.names(homonyms_1to5_reported.table) <- homonyms_1to5_model.names

Setting row names on a tibble is deprecated.

View(homonyms_1to5_reported.table)
anova(homonymy_first_order_1to5,homonymy_second_order_1to5)

refitting model(s) with ML (instead of REML)

Data: df_homonymy[df_homonymy$Generation != 0, ]
Models:
homonymy_first_order_1to5: Word_repetition ~ ot1 * GroupType * LexiconType + (1 | Unique_Participant)
homonymy_second_order_1to5: Word_repetition ~ (ot1 + ot2) * GroupType * LexiconType + (1 | 
homonymy_second_order_1to5:     Unique_Participant)
                           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)
homonymy_first_order_1to5  10 -578.15 -543.34 299.08  -598.15                         
homonymy_second_order_1to5 14 -571.96 -523.23 299.98  -599.96 1.8088      4     0.7709

sjPlot::tab_df(homonyms_reported.table,
               file="model comparison homonyms_1to5_.doc")
sjPlot::tab_df(round(coef(summary(homonymy_first_order_1to5)),3),file='homonyms_1to5_fixed_effects.doc')

9.3 Homonymy (Gen1-5): Plots of model fit

ggplot(df_homonymy[df_homonymy$Generation != 0, ], aes(Generation, Word_repetition, shape=GroupType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(homonymy_first_order_1to5), linetype=GroupType),
               fun.y=mean, geom="line") +
  ylab("Proportion of homonymy")

dev.print(pdf,'Homonymy 1to5 by group type - first order model.pdf')

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                2

ggplot(df_homonymy[df_homonymy$Generation != 0, ], aes(Generation, Word_repetition, color=GroupType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(homonymy_second_order_1to5), linetype=GroupType),
               fun.y=mean, geom="line") +
  ylab("Proportion of homonymy")

dev.print(pdf,'Homonymy 1to5  by group type - second order model.pdf')

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                2

ggplot(df_homonymy[df_homonymy$Generation != 0, ], aes(Generation, Word_repetition, shape=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(homonymy_first_order_1to5), linetype=LexiconType),
               fun.y=mean, geom="line") +
  ylab("Proportion of homonymy")

dev.print(pdf,'Homonymy 1to5 by lexicon type - first order model.pdf')

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                2

ggplot(df_homonymy[df_homonymy$Generation != 0, ], aes(Generation, Word_repetition, color=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(homonymy_second_order_1to5), linetype=LexiconType),
               fun.y=mean, geom="line") +
  ylab("Proportion of homonymy")

dev.print(pdf,'Homonymy 1to5  by lexicon type - second order model.pdf')

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                2

10 Homonymy and sound repetition

Two related questions: 1) Is sound repetition more prevelant among homonyms? (i.e., Is sound repetition driven by homonymy?) 2) Do we see the same pattern of sound repetition increase when we look only at non-homonyms?

# This bit of script identifies homonyms in each lexicon
# Identify duplicates within every subset of words
df_u = ddply(df_director, c("GroupType", "LexiconType", "GroupNumber", "Generation", "Unique_language", "Pair", "Unique_Participant"), summarise, dup = duplicated(TypedLabel))
# Paste that information back to the main dateframe
df_u2 = cbind(df_director, df_u$dup)
# List up words that have copies (i.e., homonyms)
homs = unique(subset(df_u2, df_u$dup == 'TRUE'))
# Create variable 'homonym'
df_director = merge(df_director, homs, all.x=TRUE)
colnames(df_director)[52] <- "homonym"
df_director$homonym[is.na(df_director$homonym)] <- 0
# Create a subset without homonyms
NoHoms = subset(df_director, homonym == 0)

10.1 Impact of homonymy on sound repetition

Is sound repetition more prevelant among homonyms? The analysis shows no effects of homonymy on CVC. So the answer is NO.

CVC_hom_third_order <- glmer(New_Redup_B ~ #maximal random effect structure, up to cubic term 
                  (ot1+ot2+ot3) * GroupType * LexiconType * homonym + (1|Unique_Participant) + (1|Meaning),
                  data=df_director, family=binomial, 
                  control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
CVC_hom_second_order <- glmer(New_Redup_B ~ #maximal random effect structure, up to quadratic term 
                    (ot1+ot2) * GroupType * LexiconType * homonym + (1|Unique_Participant) + (1|Meaning),
                   data=df_director, family=binomial, 
                   control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
CVC_hom_first_order <- glmer(New_Redup_B ~ #maximal random effect structure, up to linear term                          
                    ot1 * GroupType * LexiconType * homonym+ (1|Unique_Participant) + (1|Meaning),
                    data=df_director, family=binomial, 
                    control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
summary(CVC_hom_third_order)

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
 Family: binomial  ( logit )
Formula: New_Redup_B ~ (ot1 + ot2 + ot3) * GroupType * LexiconType * homonym +      (1 | Unique_Participant) + (1 | Meaning)
   Data: df_director
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

     AIC      BIC   logLik deviance df.resid 
  5031.3   5247.7  -2481.6   4963.3     4259 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3200 -0.6451 -0.4361  0.8118  4.9045 

Random effects:
 Groups             Name        Variance Std.Dev.
 Unique_Participant (Intercept) 0.7452   0.8633  
 Meaning            (Intercept) 0.2845   0.5334  
Number of obs: 4293, groups:  Unique_Participant, 156; Meaning, 18

Fixed effects:
                                     Estimate Std. Error z value Pr(>|z|)    
(Intercept)                         -0.808121   0.161868  -4.992 5.96e-07 ***
ot1                                  0.530514   0.190645   2.783  0.00539 ** 
ot2                                  0.059379   0.184687   0.322  0.74782    
ot3                                 -0.125553   0.159646  -0.786  0.43161    
GroupType1                           0.169374   0.092070   1.840  0.06582 .  
LexiconType1                        -0.049593   0.101573  -0.488  0.62537    
homonym                              0.017287   0.111668   0.155  0.87697    
ot1:GroupType1                       0.354165   0.149023   2.377  0.01747 *  
ot2:GroupType1                      -0.057817   0.147761  -0.391  0.69558    
ot3:GroupType1                      -0.124834   0.140804  -0.887  0.37530    
ot1:LexiconType1                    -0.404151   0.190573  -2.121  0.03395 *  
ot2:LexiconType1                     0.141966   0.184710   0.769  0.44214    
ot3:LexiconType1                     0.138114   0.159736   0.865  0.38724    
GroupType1:LexiconType1              0.023625   0.092056   0.257  0.79746    
ot1:homonym                         -0.428702   0.269405  -1.591  0.11154    
ot2:homonym                         -0.188312   0.259013  -0.727  0.46720    
ot3:homonym                          0.208112   0.283400   0.734  0.46274    
GroupType1:homonym                   0.001667   0.118086   0.014  0.98874    
LexiconType1:homonym                 0.157307   0.111713   1.408  0.15909    
ot1:GroupType1:LexiconType1         -0.087180   0.148985  -0.585  0.55844    
ot2:GroupType1:LexiconType1          0.069609   0.147621   0.472  0.63725    
ot3:GroupType1:LexiconType1          0.030518   0.140665   0.217  0.82824    
ot1:GroupType1:homonym              -0.312951   0.300124  -1.043  0.29707    
ot2:GroupType1:homonym              -0.020355   0.284279  -0.072  0.94292    
ot3:GroupType1:homonym               0.146422   0.294473   0.497  0.61902    
ot1:LexiconType1:homonym             0.470131   0.269451   1.745  0.08102 .  
ot2:LexiconType1:homonym            -0.151120   0.259282  -0.583  0.56000    
ot3:LexiconType1:homonym            -0.464340   0.283930  -1.635  0.10196    
GroupType1:LexiconType1:homonym      0.055324   0.118077   0.469  0.63940    
ot1:GroupType1:LexiconType1:homonym  0.311921   0.299631   1.041  0.29787    
ot2:GroupType1:LexiconType1:homonym -0.371302   0.284594  -1.305  0.19200    
ot3:GroupType1:LexiconType1:homonym -0.171318   0.294790  -0.581  0.56114    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


Correlation matrix not shown by default, as p = 32 > 12.
Use print(x, correlation=TRUE)  or
    vcov(x)        if you need it

summary(CVC_hom_second_order)

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
 Family: binomial  ( logit )
Formula: New_Redup_B ~ (ot1 + ot2) * GroupType * LexiconType * homonym +      (1 | Unique_Participant) + (1 | Meaning)
   Data: df_director
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

     AIC      BIC   logLik deviance df.resid 
  5019.5   5184.9  -2483.7   4967.5     4267 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.4627 -0.6439 -0.4370  0.8098  4.9419 

Random effects:
 Groups             Name        Variance Std.Dev.
 Unique_Participant (Intercept) 0.7694   0.8771  
 Meaning            (Intercept) 0.2838   0.5327  
Number of obs: 4293, groups:  Unique_Participant, 156; Meaning, 18

Fixed effects:
                                     Estimate Std. Error z value Pr(>|z|)    
(Intercept)                         -0.822339   0.161606  -5.089 3.61e-07 ***
ot1                                  0.532049   0.169180   3.145  0.00166 ** 
ot2                                  0.054258   0.163211   0.332  0.73956    
GroupType1                           0.183866   0.089818   2.047  0.04065 *  
LexiconType1                        -0.049034   0.101257  -0.484  0.62821    
homonym                              0.012110   0.107987   0.112  0.91071    
ot1:GroupType1                       0.354700   0.146312   2.424  0.01534 *  
ot2:GroupType1                      -0.066338   0.144222  -0.460  0.64554    
ot1:LexiconType1                    -0.436948   0.169214  -2.582  0.00982 ** 
ot2:LexiconType1                     0.183205   0.163203   1.123  0.26162    
GroupType1:LexiconType1              0.032274   0.089832   0.359  0.71940    
ot1:homonym                         -0.395407   0.267076  -1.481  0.13874    
ot2:homonym                         -0.191363   0.253039  -0.756  0.44949    
GroupType1:homonym                   0.021833   0.114595   0.191  0.84890    
LexiconType1:homonym                 0.191095   0.108050   1.769  0.07696 .  
ot1:GroupType1:LexiconType1         -0.075072   0.146296  -0.513  0.60785    
ot2:GroupType1:LexiconType1          0.063014   0.144094   0.437  0.66189    
ot1:GroupType1:homonym              -0.279199   0.297604  -0.938  0.34816    
ot2:GroupType1:homonym              -0.002066   0.278787  -0.007  0.99409    
ot1:LexiconType1:homonym             0.420616   0.267177   1.574  0.11542    
ot2:LexiconType1:homonym            -0.108324   0.253435  -0.427  0.66907    
GroupType1:LexiconType1:homonym      0.072695   0.114625   0.634  0.52595    
ot1:GroupType1:LexiconType1:homonym  0.281327   0.297159   0.947  0.34378    
ot2:GroupType1:LexiconType1:homonym -0.375634   0.279052  -1.346  0.17827    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


Correlation matrix not shown by default, as p = 24 > 12.
Use print(x, correlation=TRUE)  or
    vcov(x)        if you need it

summary(CVC_hom_first_order)

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
 Family: binomial  ( logit )
Formula: New_Redup_B ~ ot1 * GroupType * LexiconType * homonym + (1 |      Unique_Participant) + (1 | Meaning)
   Data: df_director
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

     AIC      BIC   logLik deviance df.resid 
  5007.8   5122.3  -2485.9   4971.8     4275 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.0930 -0.6449 -0.4398  0.8119  4.8887 

Random effects:
 Groups             Name        Variance Std.Dev.
 Unique_Participant (Intercept) 0.7811   0.8838  
 Meaning            (Intercept) 0.2809   0.5300  
Number of obs: 4293, groups:  Unique_Participant, 156; Meaning, 18

Fixed effects:
                                     Estimate Std. Error z value Pr(>|z|)    
(Intercept)                         -0.814540   0.159732  -5.099 3.41e-07 ***
ot1                                  0.566906   0.150954   3.755 0.000173 ***
GroupType1                           0.169273   0.085254   1.986 0.047088 *  
LexiconType1                        -0.043873   0.099073  -0.443 0.657881    
homonym                             -0.006704   0.106605  -0.063 0.949856    
ot1:GroupType1                       0.344893   0.141388   2.439 0.014714 *  
ot1:LexiconType1                    -0.338426   0.150828  -2.244 0.024846 *  
GroupType1:LexiconType1             -0.001813   0.085254  -0.021 0.983030    
ot1:homonym                         -0.313368   0.250258  -1.252 0.210504    
GroupType1:homonym                   0.009150   0.112178   0.082 0.934989    
LexiconType1:homonym                 0.161239   0.106657   1.512 0.130597    
ot1:GroupType1:LexiconType1         -0.049182   0.141349  -0.348 0.727882    
ot1:GroupType1:homonym              -0.253993   0.286781  -0.886 0.375796    
ot1:LexiconType1:homonym             0.431257   0.250513   1.721 0.085161 .  
GroupType1:LexiconType1:homonym      0.085663   0.112201   0.763 0.445179    
ot1:GroupType1:LexiconType1:homonym  0.322176   0.286629   1.124 0.261005    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


Correlation matrix not shown by default, as p = 16 > 12.
Use print(x, correlation=TRUE)  or
    vcov(x)        if you need it

CVC_hom_model.names <- c("CVC_hom first order","CVC_hom second order","CVC_hom third order")
CVC_hom_summ.table <- do.call(rbind, lapply(list(CVC_hom_first_order,CVC_hom_second_order,CVC_hom_third_order), broom::glance))
CVC_hom_table.cols <- c("df.residual", "deviance", "AIC")
CVC_hom_reported.table <- CVC_hom_summ.table[CVC_hom_table.cols]
names(CVC_hom_reported.table) <- c("Resid.Df", "Resid.Dev", "AIC")
CVC_hom_reported.table[['dAIC']] <-  with(CVC_hom_reported.table, AIC - min(AIC))
CVC_hom_reported.table[['AIC_weight']] <- with(CVC_hom_reported.table, exp(- 0.5 * dAIC) / sum(exp(- 0.5 * dAIC)))
CVC_hom_reported.table$AIC <- NULL
CVC_hom_reported.table$AIC_weight <- round(CVC_hom_reported.table$AIC_weight, 2)
CVC_hom_reported.table$dAIC <- round(CVC_hom_reported.table$dAIC, 1)
CVC_hom_reported.table$Resid.Dev <- round(CVC_hom_reported.table$Resid.Dev, 2)
row.names(CVC_hom_reported.table) <- CVC_hom_model.names

Setting row names on a tibble is deprecated.

View(CVC_hom_reported.table)
sjPlot::tab_df(CVC_hom_reported.table,
       file="model comparison CVC_hom.doc")

10.2 Sound repetition among non-homonyms

Here, an anlaysis is run using only words that don’t have any homonyms. We get pretty much the same results as the analysis with all words. So sound repetition increases regardless of homonymy.

CVC_NH_third_order <- glmer(New_Redup_B ~ #maximal random effect structure, up to cubic term 
                  (ot1+ot2+ot3) * GroupType * LexiconType + (1|Unique_Participant) + (1|Meaning),
                  data=df_director[df_director$homonym == 0, ], family=binomial, 
                  control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
CVC_NH_second_order <- glmer(New_Redup_B ~ #maximal random effect structure, up to quadratic term 
                    (ot1+ot2) * GroupType * LexiconType + (1|Unique_Participant) + (1|Meaning),
                   data=df_director[df_director$homonym == 0, ], family=binomial, 
                   control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
CVC_NH_first_order <- glmer(New_Redup_B ~ #maximal random effect structure, up to linear term                          
                    ot1 * GroupType * LexiconType + (1|Unique_Participant) + (1|Meaning),
                    data=df_director[df_director$homonym == 0, ], family=binomial, 
                    control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
summary(CVC_NH_third_order)

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
 Family: binomial  ( logit )
Formula: New_Redup_B ~ (ot1 + ot2 + ot3) * GroupType * LexiconType + (1 |      Unique_Participant) + (1 | Meaning)
   Data: df_director[df_director$homonym == 0, ]
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

     AIC      BIC   logLik deviance df.resid 
  3947.7   4057.9  -1955.8   3911.7     3358 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.1583 -0.6286 -0.4412  0.8176  4.0566 

Random effects:
 Groups             Name        Variance Std.Dev.
 Unique_Participant (Intercept) 0.7569   0.8700  
 Meaning            (Intercept) 0.2940   0.5422  
Number of obs: 3376, groups:  Unique_Participant, 154; Meaning, 18

Fixed effects:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                 -0.81889    0.16432  -4.984 6.24e-07 ***
ot1                          0.55213    0.19351   2.853  0.00433 ** 
ot2                          0.04084    0.18702   0.218  0.82712    
ot3                         -0.12438    0.16215  -0.767  0.44303    
GroupType1                   0.16855    0.09369   1.799  0.07202 .  
LexiconType1                -0.04253    0.10278  -0.414  0.67900    
ot1:GroupType1               0.34547    0.15358   2.249  0.02449 *  
ot2:GroupType1              -0.05535    0.15167  -0.365  0.71514    
ot3:GroupType1              -0.13376    0.14404  -0.929  0.35309    
ot1:LexiconType1            -0.41155    0.19331  -2.129  0.03326 *  
ot2:LexiconType1             0.14179    0.18702   0.758  0.44836    
ot3:LexiconType1             0.17699    0.16215   1.092  0.27505    
GroupType1:LexiconType1      0.01105    0.09367   0.118  0.90609    
ot1:GroupType1:LexiconType1 -0.09222    0.15349  -0.601  0.54798    
ot2:GroupType1:LexiconType1  0.07024    0.15146   0.464  0.64281    
ot3:GroupType1:LexiconType1  0.06170    0.14375   0.429  0.66774    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


Correlation matrix not shown by default, as p = 16 > 12.
Use print(x, correlation=TRUE)  or
    vcov(x)        if you need it

summary(CVC_NH_second_order)

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
 Family: binomial  ( logit )
Formula: New_Redup_B ~ (ot1 + ot2) * GroupType * LexiconType + (1 | Unique_Participant) +      (1 | Meaning)
   Data: df_director[df_director$homonym == 0, ]
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

     AIC      BIC   logLik deviance df.resid 
  3942.0   4027.8  -1957.0   3914.0     3362 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.1585 -0.6324 -0.4414  0.8190  4.0866 

Random effects:
 Groups             Name        Variance Std.Dev.
 Unique_Participant (Intercept) 0.7671   0.8758  
 Meaning            (Intercept) 0.2944   0.5426  
Number of obs: 3376, groups:  Unique_Participant, 154; Meaning, 18

Fixed effects:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                 -0.83447    0.16392  -5.091 3.57e-07 ***
ot1                          0.56774    0.17316   3.279  0.00104 ** 
ot2                          0.02413    0.16592   0.145  0.88438    
GroupType1                   0.17729    0.09219   1.923  0.05447 .  
LexiconType1                -0.03698    0.10190  -0.363  0.71669    
ot1:GroupType1               0.34903    0.14977   2.330  0.01978 *  
ot2:GroupType1              -0.06655    0.14699  -0.453  0.65069    
ot1:LexiconType1            -0.48696    0.17319  -2.812  0.00493 ** 
ot2:LexiconType1             0.21926    0.16597   1.321  0.18647    
GroupType1:LexiconType1      0.02109    0.09219   0.229  0.81903    
ot1:GroupType1:LexiconType1 -0.07581    0.14970  -0.506  0.61257    
ot2:GroupType1:LexiconType1  0.05613    0.14683   0.382  0.70226    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) ot1    ot2    GrpTy1 LxcnT1 ot1:GT1 ot2:GT1 o1:LT1 o2:LT1 GT1:LT o1:GT1:
ot1         -0.079                                                                         
ot2          0.053 -0.409                                                                  
GroupType1  -0.292 -0.049  0.051                                                           
LexiconTyp1 -0.052  0.019  0.003  0.035                                                    
ot1:GrpTyp1 -0.114  0.252  0.059  0.133 -0.011                                             
ot2:GrpTyp1  0.102  0.061  0.256 -0.155  0.032 -0.231                                      
ot1:LxcnTy1  0.016 -0.115  0.059  0.031 -0.126  0.047  -0.045                              
ot2:LxcnTy1  0.000  0.061 -0.108 -0.010  0.087 -0.044   0.023  -0.411                      
GrpTyp1:LT1  0.020  0.031 -0.010 -0.098 -0.467  0.070  -0.041  -0.050  0.051               
ot1:GT1:LT1 -0.006  0.047 -0.045  0.070 -0.184 -0.158   0.089   0.252  0.058  0.132        
ot2:GT1:LT1  0.018 -0.043  0.022 -0.041  0.170  0.089  -0.153   0.060  0.256 -0.156 -0.232

summary(CVC_NH_first_order)

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
 Family: binomial  ( logit )
Formula: New_Redup_B ~ ot1 * GroupType * LexiconType + (1 | Unique_Participant) +      (1 | Meaning)
   Data: df_director[df_director$homonym == 0, ]
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

     AIC      BIC   logLik deviance df.resid 
  3936.2   3997.4  -1958.1   3916.2     3366 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.1879 -0.6298 -0.4443  0.8141  4.0565 

Random effects:
 Groups             Name        Variance Std.Dev.
 Unique_Participant (Intercept) 0.7765   0.8812  
 Meaning            (Intercept) 0.2940   0.5422  
Number of obs: 3376, groups:  Unique_Participant, 154; Meaning, 18

Fixed effects:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                 -0.82883    0.16313  -5.081 3.76e-07 ***
ot1                          0.59170    0.15573   3.800 0.000145 ***
GroupType1                   0.16758    0.09083   1.845 0.065048 .  
LexiconType1                -0.04619    0.10063  -0.459 0.646240    
ot1:GroupType1               0.33463    0.14472   2.312 0.020759 *  
ot1:LexiconType1            -0.39614    0.15553  -2.547 0.010865 *  
GroupType1:LexiconType1      0.01167    0.09084   0.128 0.897811    
ot1:GroupType1:LexiconType1 -0.07951    0.14466  -0.550 0.582574    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) ot1    GrpTy1 LxcnT1 ot1:GT1 o1:LT1 GT1:LT
ot1         -0.082                                           
GroupType1  -0.285  0.002                                    
LexiconTyp1 -0.061  0.018  0.054                             
ot1:GrpTyp1 -0.101  0.372  0.096 -0.011                      
ot1:LxcnTy1  0.014 -0.090  0.034 -0.131  0.009               
GrpTyp1:LT1  0.031  0.035 -0.123 -0.458  0.076   0.001       
ot1:GT1:LT1 -0.006  0.009  0.076 -0.162 -0.118   0.372  0.096

CVC_NH_model.names <- c("CVC_NH first order","CVC_NH second order","CVC_NH third order")
CVC_NH_summ.table <- do.call(rbind, lapply(list(CVC_NH_first_order,CVC_NH_second_order,CVC_NH_third_order), broom::glance))
CVC_NH_table.cols <- c("df.residual", "deviance", "AIC")
CVC_NH_reported.table <- CVC_NH_summ.table[CVC_NH_table.cols]
names(CVC_NH_reported.table) <- c("Resid.Df", "Resid.Dev", "AIC")
CVC_NH_reported.table[['dAIC']] <-  with(CVC_NH_reported.table, AIC - min(AIC))
CVC_NH_reported.table[['AIC_weight']] <- with(CVC_NH_reported.table, exp(- 0.5 * dAIC) / sum(exp(- 0.5 * dAIC)))
CVC_NH_reported.table$AIC <- NULL
CVC_NH_reported.table$AIC_weight <- round(CVC_NH_reported.table$AIC_weight, 2)
CVC_NH_reported.table$dAIC <- round(CVC_NH_reported.table$dAIC, 1)
CVC_NH_reported.table$Resid.Dev <- round(CVC_NH_reported.table$Resid.Dev, 2)
row.names(CVC_NH_reported.table) <- CVC_NH_model.names

Setting row names on a tibble is deprecated.

View(CVC_NH_reported.table)
sjPlot::tab_df(CVC_NH_reported.table,
       file="model comparison CVC_NH.doc")

Here’s a plot showing the proportion of CVC repetition in non-homonyms

ggplot(NoHoms, aes(Generation, New_Redup_B, shape=GroupType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(CVC_NH_first_order), linetype=GroupType),
               fun.y=mean, geom="line") +
  ylab("Consonant repetition")+ 
  ggtitle('Consonant repetition in non-homonyms by group type')

dev.print(pdf, 'Consonant repetition in non-homonyms by group type - first order model.pdf')

quartz_off_screen 
                2

ggplot(NoHoms, aes(Generation, New_Redup_B, shape=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(CVC_NH_first_order), linetype=LexiconType),
               fun.y=mean, geom="line") +
  ylab("Consonant repetition")+ 
  ggtitle('Consonant repetition in non-homonyms by lexicon size')

dev.print(pdf, 'Consonant repetition in non-homonyms by lexicon size - first order model.pdf')

quartz_off_screen 
                2

11 Word length and sound repetition

A query raised by Jenny is whether the repetition effects could be driven by word length - so longer words will be more likely to contain repetitions and words get longer in the chains than in the closed groups.

As the analysis below shows, there’s obviously a clear association between word length and sound repetition, although the direction of the causality could be rep -> length.

df_director_generations_1_to_5$LabelLength = nchar(as.character(df_director_generations_1_to_5$TypedLabel))
RepXLength = ddply(df_director_generations_1_to_5, c("LabelLength"), summarise, mean.rep = mean(New_Redup_B))
ggplot(RepXLength, aes(x=LabelLength, y=mean.rep)) +
    geom_point(shape=1) +    
    geom_smooth(method=lm)

summary(lm(LabelLength ~ mean.rep, data=RepXLength))


Call:
lm(formula = LabelLength ~ mean.rep, data = RepXLength)

Residuals:
    Min      1Q  Median      3Q     Max 
-0.7622 -0.6932 -0.4123  0.1943  2.3114 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   2.9359     0.6501   4.516  0.00196 ** 
mean.rep      8.8263     1.0806   8.168 3.76e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.051 on 8 degrees of freedom
Multiple R-squared:  0.8929,    Adjusted R-squared:  0.8795 
F-statistic: 66.71 on 1 and 8 DF,  p-value: 3.759e-05

So, can this explain why we find a GroupType or LexiconSize effect on repetition? Here’s a figure showing the average label length by GroupType and LexiconSize, followed by a set of GCA analyses. The linear analysis (best fit) shows that label length increases over time, but this increase doesn’t interact with GroupType or LexiconSize.

df_director_plot$LabelLength = nchar(as.character(df_director$TypedLabel))
ggplot(df_director_plot, aes(Generation, LabelLength, group = group_id, colour=GroupType, linetype=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="line") +
  ylab("Label length")+ 
  ggtitle('Label length by group type and lexicon size')

dev.print(pdf, 'Label length by group and lexicon.pdf')

quartz_off_screen 
                2

LabelLength_third_order <- lmer(LabelLength ~ #maximal random effect structure, up to cubic term 
                  (ot1+ot2+ot3) * GroupType * LexiconType + (1|Unique_Participant) + (1|Meaning),
                  data=df_director_plot, 
                  control=lmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
LabelLength_second_order <- lmer(LabelLength ~ #maximal random effect structure, up to quadratic term 
                    (ot1+ot2) * GroupType * LexiconType + (1|Unique_Participant) + (1|Meaning),
                   data=df_director_plot, 
                   control=lmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
LabelLength_first_order <- lmer(LabelLength ~ #maximal random effect structure, up to linear term                          
                    ot1 * GroupType * LexiconType + (1|Unique_Participant) + (1|Meaning),
                    data=df_director_plot,
                    control=lmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
summary(LabelLength_third_order)

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: LabelLength ~ (ot1 + ot2 + ot3) * GroupType * LexiconType + (1 |      Unique_Participant) + (1 | Meaning)
   Data: df_director_plot
Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

REML criterion at convergence: 13516.3

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.2978 -0.9185  0.0555  0.7996  4.3738 

Random effects:
 Groups             Name        Variance Std.Dev.
 Unique_Participant (Intercept) 0.1601   0.4001  
 Meaning            (Intercept) 0.1472   0.3836  
 Residual                       2.3832   1.5437  
Number of obs: 3593, groups:  Unique_Participant, 144; Meaning, 18

Fixed effects:
                             Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)                   5.81076    0.12002  42.70091  48.415   <2e-16 ***
ot1                           0.08351    0.22632 382.78778   0.369    0.712    
ot2                           0.09782    0.20861 382.45038   0.469    0.639    
ot3                          -0.12489    0.15179 382.47872  -0.823    0.411    
GroupType1                    0.01926    0.07815 159.29109   0.246    0.806    
LexiconType1                  0.07036    0.07894 165.71974   0.891    0.374    
ot1:GroupType1                0.24891    0.22632 382.78755   1.100    0.272    
ot2:GroupType1               -0.09212    0.20861 382.45109  -0.442    0.659    
ot3:GroupType1                0.07308    0.15179 382.47881   0.481    0.630    
ot1:LexiconType1              0.02873    0.22632 382.78778   0.127    0.899    
ot2:LexiconType1              0.08342    0.20861 382.45038   0.400    0.689    
ot3:LexiconType1             -0.02707    0.15179 382.47872  -0.178    0.859    
GroupType1:LexiconType1      -0.01431    0.07815 159.29109  -0.183    0.855    
ot1:GroupType1:LexiconType1  -0.10883    0.22632 382.78755  -0.481    0.631    
ot2:GroupType1:LexiconType1   0.21399    0.20861 382.45109   1.026    0.306    
ot3:GroupType1:LexiconType1  -0.03872    0.15179 382.47881  -0.255    0.799    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


Correlation matrix not shown by default, as p = 16 > 12.
Use print(x, correlation=TRUE)  or
    vcov(x)        if you need it

summary(LabelLength_second_order)

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: LabelLength ~ (ot1 + ot2) * GroupType * LexiconType + (1 | Unique_Participant) +      (1 | Meaning)
   Data: df_director_plot
Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

REML criterion at convergence: 13509.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3002 -0.9184  0.0567  0.7941  4.3754 

Random effects:
 Groups             Name        Variance Std.Dev.
 Unique_Participant (Intercept) 0.1566   0.3957  
 Meaning            (Intercept) 0.1472   0.3836  
 Residual                       2.3825   1.5435  
Number of obs: 3593, groups:  Unique_Participant, 144; Meaning, 18

Fixed effects:
                              Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)                   5.767291   0.107556  28.241490  53.621   <2e-16 ***
ot1                           0.239329   0.123528 393.791328   1.937   0.0534 .  
ot2                          -0.044399   0.116430 393.374745  -0.381   0.7032    
GroupType1                    0.044712   0.057188  95.010423   0.782   0.4363    
LexiconType1                  0.060894   0.058245 102.250965   1.045   0.2983    
ot1:GroupType1                0.157709   0.123528 393.790878   1.277   0.2025    
ot2:GroupType1               -0.008871   0.116430 393.375363  -0.076   0.9393    
ot1:LexiconType1              0.062535   0.123528 393.791328   0.506   0.6130    
ot2:LexiconType1              0.052586   0.116430 393.374745   0.452   0.6518    
GroupType1:LexiconType1      -0.027742   0.057188  95.010423  -0.485   0.6287    
ot1:GroupType1:LexiconType1  -0.060586   0.123528 393.790878  -0.490   0.6241    
ot2:GroupType1:LexiconType1   0.169946   0.116430 393.375363   1.460   0.1452    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) ot1    ot2    GrpTy1 LxcnT1 ot1:GT1 ot2:GT1 o1:LT1 o2:LT1 GT1:LT o1:GT1:
ot1         -0.214                                                                         
ot2          0.207 -0.646                                                                  
GroupType1  -0.180 -0.128  0.125                                                           
LexiconTyp1 -0.054  0.052 -0.051  0.000                                                    
ot1:GrpTyp1 -0.068  0.320 -0.207 -0.402  0.000                                             
ot2:GrpTyp1  0.066 -0.207  0.321  0.389  0.000 -0.646                                      
ot1:LxcnTy1  0.029 -0.134  0.087  0.000 -0.395  0.000   0.000                              
ot2:LxcnTy1 -0.028  0.087 -0.135  0.000  0.382  0.000   0.000  -0.646                      
GrpTyp1:LT1  0.000  0.000  0.000 -0.067 -0.332  0.054  -0.052  -0.128  0.125               
ot1:GT1:LT1  0.000  0.000  0.000  0.054 -0.126 -0.134   0.087   0.320 -0.207 -0.402        
ot2:GT1:LT1  0.000  0.000  0.000 -0.052  0.122  0.087  -0.135  -0.207  0.321  0.389 -0.646

summary(LabelLength_first_order)

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: LabelLength ~ ot1 * GroupType * LexiconType + (1 | Unique_Participant) +      (1 | Meaning)
   Data: df_director_plot
Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

REML criterion at convergence: 13502

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.2831 -0.9189  0.0570  0.7992  4.3589 

Random effects:
 Groups             Name        Variance Std.Dev.
 Unique_Participant (Intercept) 0.1555   0.3943  
 Meaning            (Intercept) 0.1471   0.3836  
 Residual                       2.3818   1.5433  
Number of obs: 3593, groups:  Unique_Participant, 144; Meaning, 18

Fixed effects:
                             Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)                   5.77577    0.10515  25.90582  54.929   <2e-16 ***
ot1                           0.20880    0.09422 400.08135   2.216   0.0272 *  
GroupType1                    0.04636    0.05254  80.49492   0.882   0.3802    
LexiconType1                  0.05089    0.05369  87.80078   0.948   0.3458    
ot1:GroupType1                0.15172    0.09422 400.08027   1.610   0.1081    
ot1:LexiconType1              0.09840    0.09422 400.08135   1.044   0.2969    
GroupType1:LexiconType1      -0.06024    0.05254  80.49492  -1.147   0.2550    
ot1:GroupType1:LexiconType1   0.05586    0.09422 400.08027   0.593   0.5536    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) ot1    GrpTy1 LxcnT1 ot1:GT1 o1:LT1 GT1:LT
ot1         -0.107                                           
GroupType1  -0.227 -0.068                                    
LexiconTyp1 -0.049  0.028  0.000                             
ot1:GrpTyp1 -0.034  0.320 -0.214  0.000                      
ot1:LxcnTy1  0.015 -0.134  0.000 -0.210  0.000               
GrpTyp1:LT1  0.000  0.000 -0.056 -0.445  0.029  -0.068       
ot1:GT1:LT1  0.000  0.000  0.029 -0.067 -0.134   0.320 -0.214

LabelLength_model.names <- c("LabelLength first order","LabelLength second order","LabelLength third order")
LabelLength_summ.table <- do.call(rbind, lapply(list(LabelLength_first_order,LabelLength_second_order,LabelLength_third_order), broom::glance))
LabelLength_table.cols <- c("df.residual", "deviance", "AIC")
LabelLength_reported.table <- LabelLength_summ.table[LabelLength_table.cols]
names(LabelLength_reported.table) <- c("Resid.Df", "Resid.Dev", "AIC")
LabelLength_reported.table[['dAIC']] <-  with(LabelLength_reported.table, AIC - min(AIC))
LabelLength_reported.table[['AIC_weight']] <- with(LabelLength_reported.table, exp(- 0.5 * dAIC) / sum(exp(- 0.5 * dAIC)))
LabelLength_reported.table$AIC <- NULL
LabelLength_reported.table$AIC_weight <- round(LabelLength_reported.table$AIC_weight, 2)
LabelLength_reported.table$dAIC <- round(LabelLength_reported.table$dAIC, 1)
LabelLength_reported.table$Resid.Dev <- round(LabelLength_reported.table$Resid.Dev, 2)
row.names(LabelLength_reported.table) <- LabelLength_model.names

Setting row names on a tibble is deprecated.

View(LabelLength_reported.table)
sjPlot::tab_df(LabelLength_reported.table,
       file="model comparison LabelLength.doc")

For good measure, we can rerun our CVC analysis with LabelLength thrown in as a fixed effect (just the first-order analysis). Although LabelLength gobbles up the effects, there’s still enough left in the Time x GroupType and Time x LexiconSize interactions to give us assurance that these factors cannot be explained away.

CVC_LabelLength_first_order <- glmer(New_Redup_B ~ #maximal random effect structure, up to linear term                          
                    ot1 * GroupType * LexiconType + LabelLength + (1|Unique_Participant) + (1|Meaning),
                    data=df_director_plot, family=binomial, 
                    control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
summary(CVC_LabelLength_first_order)

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
 Family: binomial  ( logit )
Formula: New_Redup_B ~ ot1 * GroupType * LexiconType + LabelLength + (1 |      Unique_Participant) + (1 | Meaning)
   Data: df_director_plot
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

     AIC      BIC   logLik deviance df.resid 
  3989.7   4057.7  -1983.8   3967.7     3582 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.7899 -0.6254 -0.3697  0.7177  3.9017 

Random effects:
 Groups             Name        Variance Std.Dev.
 Unique_Participant (Intercept) 0.8765   0.9362  
 Meaning            (Intercept) 0.2977   0.5456  
Number of obs: 3593, groups:  Unique_Participant, 144; Meaning, 18

Fixed effects:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                 -2.94850    0.23698 -12.442  < 2e-16 ***
ot1                          0.52922    0.17541   3.017  0.00255 ** 
GroupType1                   0.18926    0.11488   1.647  0.09947 .  
LexiconType1                -0.04797    0.11606  -0.413  0.67936    
LabelLength                  0.35762    0.02676  13.363  < 2e-16 ***
ot1:GroupType1               0.31927    0.17535   1.821  0.06865 .  
ot1:LexiconType1            -0.35218    0.17530  -2.009  0.04454 *  
GroupType1:LexiconType1      0.04995    0.11486   0.435  0.66364    
ot1:GroupType1:LexiconType1 -0.04625    0.17522  -0.264  0.79182    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) ot1    GrpTy1 LxcnT1 LblLng ot1:GT1 o1:LT1 GT1:LT
ot1         -0.092                                                  
GroupType1  -0.264 -0.101                                           
LexiconTyp1 -0.016  0.023 -0.003                                    
LabelLength -0.681 -0.004  0.004 -0.013                             
ot1:GrpTyp1 -0.046  0.518 -0.193  0.003 -0.007                      
ot1:LxcnTy1  0.032 -0.083  0.003 -0.191 -0.029 -0.021               
GrpTyp1:LT1 -0.015  0.002 -0.029 -0.535  0.020  0.023  -0.101       
ot1:GT1:LT1  0.011 -0.021  0.024 -0.100 -0.013 -0.083   0.518 -0.193

---
title: 'Reduplication study: GCA analysis and model comparisons - New Coding'
author: "Aitor San Jose, Mits Ota, Kenny Smith"
date: "13 September 2019"
output:
  html_document:
    df_print: paged
    toc: yes
  html_notebook:
    number_sections: yes
    toc: yes
---


# Preamble
These are GCA analyses and model comparisons run for the reduplication study. Originally in raw R script, now in R Notebook format. It saves tables as .doc files and plots as .pdf files. *This one uses the new coding of phonological repeition implemented by Kenny*.


# Packages
```{r warning=FALSE}
library(ggplot2)
library(broom)
library(lme4)
library(bbmle)
library(lmerTest)
library(sjPlot)
library(plyr)
```

# Data preparation
## Data read-in
This reads in the new dataset that includes the new coding of syllable and consonant repetition. But the script replaces the old equivalents (Adj_B, New_Redup_B) with the new variables (SyllableRepetition, ConsonantRepetition). 
```{r}
df = read.table(file="data_june_2018_recoded2.csv", na.strings=c("", "NA"), sep=",", header=TRUE)
df = subset(df, select = -c(New_Redup_B, Adj_B)) 
colnames(df)[c(46, 47)] <- c("Adj_B", "New_Redup_B") 
colnames(df)[6] <- "Unique_language"
df$Trial_Iteration[is.na(df$Trial_Iteration)] <- 0 #make NAs 0 for next line to work
df <-subset(df,Trial_Iteration != 1)  # keep seed languages and second round of iterations
# df <- subset(df, Transmitter == 1) # uncomment to keep only the data from the participants whose language was used to train the following generation

df_director<-subset(df,Stage!='interactM') # includes seed languages
df_matcher<-subset(df,Stage=='interactM') # includes only generations 1 to 5, since there is no accuracy for seed languages
df_director_generations_1_to_5 <- subset(df_director, Generation > 0) #excludes seed languages
```

## Sum-coding
```{r}
contrasts(df_director$LexiconType) = contr.sum(2)
contrasts(df_director$GroupType) = contr.sum(2)
contrasts(df_matcher$LexiconType) = contr.sum(2)
contrasts(df_matcher$GroupType) = contr.sum(2)
```

## Adding 3rd-order polynomials for GCA

```{r}
df_director$timebin = df_director$Generation + 1
t = poly(unique(df_director$timebin), 3)
df_director[,paste("ot", 1:3, sep="")] <- t[df_director$timebin, 1:3]

df_matcher$timebin = df_matcher$Generation
t = poly(unique(df_matcher$timebin), 3)
df_matcher[,paste("ot", 1:3, sep="")] <- t[df_matcher$timebin, 1:3]
```


# Analysis of consonant repetition ("CVC")

## CVC: Descriptive plot
```{r}
groups = ddply(df_director, c("GroupType", "LexiconType", "GroupNumber"), summarise, n=length(Block))
groups$group_id = factor(LETTERS[1:24])
df_director_plot = merge(df_director, groups, all.x = TRUE)
ggplot(df_director_plot, aes(Generation, New_Redup_B, group = group_id, colour=GroupType, linetype=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="line") +
  ylab("Consonant repetition")+ 
  ggtitle('Consonant repetition by group type and lexicon size')
dev.print(pdf, 'Consonant repetition by group type - dog dinner.pdf')
```


## CVC: Full models
```{r}
# CVC_third_order <- glmer(New_Redup_B ~ #maximal random effect structure, up to cubic term 
#                 (ot1+ot2+ot3) * GroupType * LexiconType + (1+ot1+ot2+ot3|Unique_Participant) + 
#                 (1+ot1+ot2+ot3 |Meaning) + (1+ot1+ot2+ot3|Pair), data=df_director, family=binomial, 
#                 control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))

#CVC_second_order <- glmer(New_Redup_B ~ #maximal random effect structure, up to quadratic term 
#                 (ot1+ot2) * GroupType * LexiconType + (1+ot1+ot2|Unique_Participant) + 
#                 (1+ot1+ot2 |Meaning) + (1+ot1+ot2|Pair), data=df_director, family=binomial, 
#                 control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))

#CVC_first_order <- glmer(New_Redup_B ~ #maximal random effect structure, up to linear term 
#                 ot1 * GroupType * LexiconType + (1+ot1|Unique_Participant) + (1+ot1 |Meaning) + 
#                 (1+ot1+ot2|Pair), data=df_director, family=binomial, 
#                 control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))    
```

## CVC: Reduced models              
The full models above all result in singular fit. The models converge without singular fit when all the random slopes are removed and 'Pair' is also removed as a random effect.Below are models with these terms dropped.
```{r}
CVC_third_order <- glmer(New_Redup_B ~ #maximal random effect structure, up to cubic term 
                  (ot1+ot2+ot3) * GroupType * LexiconType + (1|Unique_Participant) + (1|Meaning),
                  data=df_director, family=binomial, 
                  control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))

CVC_second_order <- glmer(New_Redup_B ~ #maximal random effect structure, up to quadratic term 
                    (ot1+ot2) * GroupType * LexiconType + (1|Unique_Participant) + (1|Meaning),
                   data=df_director, family=binomial, 
                   control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))

CVC_first_order <- glmer(New_Redup_B ~ #maximal random effect structure, up to linear term                          
                    ot1 * GroupType * LexiconType + (1|Unique_Participant) + (1|Meaning),
                    data=df_director, family=binomial, 
                    control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
summary(CVC_third_order)
summary(CVC_second_order)
summary(CVC_first_order)
```

## CVC: Model comparison
```{r}
CVC_model.names <- c("CVC first order","CVC second order","CVC third order")
CVC_summ.table <- do.call(rbind, lapply(list(CVC_first_order,CVC_second_order,CVC_third_order), broom::glance))
CVC_table.cols <- c("df.residual", "deviance", "AIC")
CVC_reported.table <- CVC_summ.table[CVC_table.cols]
names(CVC_reported.table) <- c("Resid.Df", "Resid.Dev", "AIC")
CVC_reported.table[['dAIC']] <-  with(CVC_reported.table, AIC - min(AIC))
CVC_reported.table[['AIC_weight']] <- with(CVC_reported.table, exp(- 0.5 * dAIC) / sum(exp(- 0.5 * dAIC)))
CVC_reported.table$AIC <- NULL
CVC_reported.table$AIC_weight <- round(CVC_reported.table$AIC_weight, 2)
CVC_reported.table$dAIC <- round(CVC_reported.table$dAIC, 1)
CVC_reported.table$Resid.Dev <- round(CVC_reported.table$Resid.Dev, 2)
row.names(CVC_reported.table) <- CVC_model.names
View(CVC_reported.table)
sjPlot::tab_df(CVC_reported.table,
       file="model comparison CVC.doc")
```

## CVC: Plots showing model fit of 1st and 2nd order models by group type
```{r}
ggplot(df_director, aes(Generation, New_Redup_B, shape=GroupType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(CVC_first_order), linetype=GroupType),
               fun.y=mean, geom="line") +
  ylab("Consonant repetition")+ 
  ggtitle('Consonant repetition by group type - first order model')
dev.print(pdf, 'Consonant repetition by group type - first order model.pdf')

ggplot(df_director, aes(Generation, New_Redup_B, color=GroupType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(CVC_second_order), linetype=GroupType),
               fun.y=mean, geom="line") +
  ylab("CVC repetition")+ 
  ggtitle('CVC repetition by group type - second order model')
dev.print(pdf,'CVC repetition by group type - second order model.pdf')

# sjPlot::tab_df(round(coef(summary(CVC_first_order)),3),file='CVC_fixed_effects.doc')
# sjPlot::tab_df(as.numeric(VarCorr(CVC_first_order)),file='CVC_random_effects.doc')
```

## CVC: Plots showing model fit of 1st and 2nd order models by lexicon type
```{r}
ggplot(df_director, aes(Generation, New_Redup_B, shape=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(CVC_first_order), linetype=LexiconType),
               fun.y=mean, geom="line") +
  ylab("CVC repetition") + 
  ggtitle('CVC repetition by lexicon type - first order model')
dev.print(pdf,'CVC repetition by lexicon type - first order model.pdf')


ggplot(df_director, aes(Generation, New_Redup_B, shape=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(CVC_second_order), linetype=LexiconType),
               fun.y=mean, geom="line") +
  ylab("CVC repetition") + 
  ggtitle('CVC repetition by lexicon type - second order model')
dev.print(pdf,'CVC repetition by lexicon type - second order model.pdf')
```

# Analysis of adjacent syllable repetition

## Descriptive plot
```{r}
ggplot(df_director_plot, aes(Generation, Adj_B, group = group_id, colour=GroupType, linetype=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="line") +
  ylab("Syllable repetition")+ 
  ggtitle('Syllable repetition by group type and lexicon size')
dev.print(pdf, 'Syllable repetition by group type - dog dinner.pdf')
```

## Syllable repetition: Full models
```{r}
# ADJ_third_order <- glmer(Adj_B ~ #maximal random effect structure, up to cubic term 
#                   (ot1+ot2+ot3) * GroupType * LexiconType + (1+ot1+ot2+ot3|Unique_Participant) + 
#                   (1+ot1+ot2+ot3 |Meaning) + (1+ot1+ot2+ot3|Pair), data=df_director, family=binomial, 
#                   control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))


# ADJ_second_order <- glmer(Adj_B ~ #maximal random effect structure, up to quadratic term 
#                   (ot1+ot2) * GroupType * LexiconType + (1+ot1+ot2|Unique_Participant) + 
#                   (1+ot1+ot2 |Meaning) + (1+ot1+ot2|Pair),
#                   data=df_director, family=binomial, 
#                   control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))


# ADJ_first_order <- glmer(Adj_B ~ #maximal random effect structure, up to linear term 
#                   ot1 * GroupType * LexiconType + (1+ot1|Unique_Participant) + 
#                   (1+ot1 |Meaning) + (1+ot1|Pair), data=df_director, family=binomial, 
#                   control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
```

## Syllable repetition: Reduced models
The full models above also result in singular fit. So they are reduced below.
```{r}
ADJ_third_order <- glmer(Adj_B ~ #maximal random effect structure, up to cubic term 
                         (ot1+ot2+ot3) * GroupType * LexiconType + (1|Unique_Participant) + 
                           (1|Meaning), data=df_director, family=binomial, 
                         control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))


ADJ_second_order <- glmer(Adj_B ~ #maximal random effect structure, up to quadratic term 
                      (ot1+ot2) * GroupType * LexiconType + (1|Unique_Participant) + 
                        (1|Meaning), data=df_director, family=binomial, 
                      control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))


ADJ_first_order <- glmer(Adj_B ~ #maximal random effect structure, up to linear term 
                        ot1 * GroupType * LexiconType + (1|Unique_Participant) + 
                          (1|Meaning), data=df_director, family=binomial, 
                        control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
summary(ADJ_third_order)
summary(ADJ_second_order)
summary(ADJ_first_order)
```


## Syllable repetition: Model comparison
```{r}
ADJ_model.names <- c("ADJ first order","ADJ second order","ADJ third order")
ADJ_summ.table <- do.call(rbind, lapply(list(ADJ_first_order,ADJ_second_order,ADJ_third_order), broom::glance))
ADJ_table.cols <- c("df.residual", "deviance", "AIC")
ADJ_reported.table <- ADJ_summ.table[ADJ_table.cols]
names(ADJ_reported.table) <- c("Resid.Df", "Resid.Dev", "AIC")
ADJ_reported.table[['dAIC']] <-  with(ADJ_reported.table, AIC - min(AIC))
ADJ_reported.table[['AIC_weight']] <- with(ADJ_reported.table, exp(- 0.5 * dAIC) / sum(exp(- 0.5 * dAIC)))
ADJ_reported.table$AIC <- NULL
ADJ_reported.table$AIC_weight <- round(ADJ_reported.table$AIC_weight, 2)
ADJ_reported.table$dAIC <- round(ADJ_reported.table$dAIC, 1)
ADJ_reported.table$Resid.Dev <- round(ADJ_reported.table$Resid.Dev, 2)

row.names(ADJ_reported.table) <- ADJ_model.names
View(ADJ_reported.table)

sjPlot::tab_df(ADJ_reported.table,
               file="model comparison _ADJ.doc")

sjPlot::tab_df(round(coef(summary(ADJ_first_order)),3),file='ADJ_fixed_effects.doc')
```


## Syllable repetition: Plots showing model fit of 1st and 2nd order models by group type
```{r}
df_director_adj_plots <- subset(df_director, !(is.na(df_director$Adj_B)))

  
ggplot(df_director_adj_plots, aes(Generation, Adj_B, shape=GroupType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(ADJ_first_order), linetype=GroupType),
               fun.y=mean, geom="line") +
  ylab("Adjacent repetition")+ 
  ggtitle('Adjacent repetition by group type - first order model')
dev.print(pdf,'Adjacent repetition by group type - first order model.pdf')

ggplot(df_director_adj_plots, aes(Generation, Adj_B, color=GroupType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(ADJ_second_order), linetype=GroupType),
               fun.y=mean, geom="line") +
  ylab("Adjacent Repetition") +
  ggtitle('Adjacent repetition by group type - second order model')
dev.print(pdf,'Adjacent repetition by group type - second order model.pdf')
```

## Syllable repetition: Plots showing model fit of 1st and 2nd order models by lexicon type
```{r}
ggplot(df_director_adj_plots, aes(Generation, Adj_B, color=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(ADJ_first_order), linetype=LexiconType),
               fun.y=mean, geom="line") +
  ylab("Adjacent repetition")+ 
  ggtitle('Adjacent repetition by lexicon type - first order model')
dev.print(pdf,'Adjacent repetition by lexicon type - first order model.pdf')


ggplot(df_director_adj_plots, aes(Generation, Adj_B, color=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(ADJ_second_order), linetype=LexiconType),
               fun.y=mean, geom="line") +
  ylab("Adjacent repetition")+ 
  ggtitle('Adjacent repetition by lexicon type - second order model')
dev.print(pdf,'Adjacent repetition by lexicon type - second order model.pdf')
```

# Accuracy
## Descriptive plot
```{r}
groups2 = ddply(df_matcher, c("GroupType", "LexiconType", "GroupNumber"), summarise, n=length(Block))
groups2$group_id = factor(LETTERS[1:24])
df_matcher_plot = merge(df_matcher, groups, all.x = TRUE)
ggplot(df_matcher_plot, aes(Generation, Score, group = group_id, colour=GroupType, linetype=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="line") +
  ylab("Accuracy")+ 
  ggtitle('Accuracy group type and lexicon size')
dev.print(pdf, 'Accuracy by group type - dog dinner.pdf')
```

## Accuracy: Full models
```{r}
#ACC_first_order <- glmer(Score ~ #maximal random effect structure, up to linear term 
#                        ot1 * GroupType * LexiconType  + (1+ot1|Unique_Participant) + 
#                       (1+ot1 |Meaning) + (1+ot1|Pair), data=df_matcher, family=binomial, 
#                       control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))


#ACC_second_order <- glmer(Score ~ #maximal random effect structure, up to quadratic term 
#                     (ot1+ot2) * GroupType * LexiconType + (1+ot1+ot2|Unique_Participant) + 
#                     (1+ot1+ot2 |Meaning) + (1+ot1+ot2|Pair), data=df_matcher, family=binomial, 
#                     control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))

#ACC_third_order <- glmer(Score ~ #maximal random effect structure, up to cubic term 
#                     (ot1+ot2+ot3) * GroupType * LexiconType + (1+ot1+ot2+ot3|Unique_Participant) + 
#                   (1+ot1+ot2+ot3 |Meaning) + (1+ot1+ot2+ot3|Pair), data=df_matcher, family=binomial, 
#                   control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
```

## Accuracy: Reduced models
The full models above also result in singular fit. So they are reduced below.
```{r}
ACC_first_order <- glmer(Score ~ #maximal random effect structure, up to linear term 
                         ot1 * GroupType * LexiconType + (1|Unique_Participant) + (1|Meaning),
                       data=df_matcher, family=binomial, 
                       control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))

ACC_second_order <- glmer(Score ~ #maximal random effect structure, up to quadratic term 
                      (ot1+ot2) * GroupType * LexiconType + (1|Unique_Participant) + (1|Meaning),
                    data=df_matcher, family=binomial, 
                    control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))

ACC_third_order <- glmer(Score ~ #maximal random effect structure, up to cubic term 
                      (ot1+ot2+ot3) * GroupType * LexiconType + (1|Unique_Participant) + (1|Meaning),
                    data=df_matcher, family=binomial, 
                    control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
summary(ACC_third_order)
summary(ACC_second_order)
summary(ACC_first_order)
```


## Accuracy: Model comparison
```{r}
ACC_model.names <- c("ACC first order","ACC second order","ACC third order")
ACC_summ.table <- do.call(rbind, lapply(list(ACC_first_order,ACC_second_order,ACC_third_order), broom::glance))
ACC_table.cols <- c("df.residual", "deviance", "AIC")
ACC_reported.table <- ACC_summ.table[ACC_table.cols]
names(ACC_reported.table) <- c("Resid.Df", "Resid.Dev", "AIC")
ACC_reported.table[['dAIC']] <-  with(ACC_reported.table, AIC - min(AIC))
ACC_reported.table[['AIC_weight']] <- with(ACC_reported.table, exp(- 0.5 * dAIC) / sum(exp(- 0.5 * dAIC)))
ACC_reported.table$AIC <- NULL
ACC_reported.table$dAIC <- round(ACC_reported.table$dAIC, 1)
ACC_reported.table$AIC_weight <- round(ACC_reported.table$AIC_weight, 2)
ACC_reported.table$Resid.Dev <- round(ACC_reported.table$Resid.Dev, 2)
row.names(ACC_reported.table) <- ACC_model.names
View(ACC_reported.table)
sjPlot::tab_df(ACC_reported.table,
               file="model comparison _ACC.doc")
```


## Accuracy: Plots showing model fit of 1st and 2nd order models by group type
```{r}
ggplot(df_matcher, aes(Generation, Score, shape=GroupType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(ACC_first_order), linetype=GroupType),
               fun.y=mean, geom="line") +
  ylab("Accuracy")+ 
  ggtitle('Accuracy by group type - first order model')
dev.print(pdf,'Accuracy by group type - first order model.pdf')

ggplot(df_matcher, aes(Generation, Score, color=GroupType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(ACC_second_order), linetype=GroupType),
               fun.y=mean, geom="line") +
  ylab("Accuracy")+ 
  ggtitle('Accuracy by group type - second order model')
dev.print(pdf,'Accuracy by group type - second order model.pdf')

```


## Accuracy: Plots showing model fit of 1st and 2nd order models by lexicon type
```{r}
ggplot(df_matcher, aes(Generation, Score, shape=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(ACC_first_order), linetype=LexiconType),
               fun.y=mean, geom="line") +
  ylab("Accuracy")+ 
  ggtitle('Accuracy by lexicon type - first order model')
dev.print(pdf,'Accuracy by lexicon type - first order model.pdf')

ggplot(df_matcher, aes(Generation, Score, shape=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(ACC_second_order), linetype=LexiconType),
               fun.y=mean, geom="line") +
  ylab("Accuracy")+ 
  ggtitle('Accuracy by lexicon type - second order model')
dev.print(pdf,'Accuracy by lexicon type - second order model.pdf')

sjPlot::tab_df(round(coef(summary(ACC_first_order)),3),file='ACC_fixed_effects.doc')
```

# Homonymy

## Coding homonymy
These are based on iteration 2 only. Homonymy is calculated as proportions.
```{r}
df_homonymy<-setNames(aggregate(df_director_generations_1_to_5$New_Redup_B, by=list(df_director_generations_1_to_5$GroupType,df_director_generations_1_to_5$LexiconType,df_director_generations_1_to_5$GroupNumber,df_director_generations_1_to_5$Generation,df_director_generations_1_to_5$Unique_language,df_director_generations_1_to_5$Pair, df_director_generations_1_to_5$Unique_Participant), FUN=mean),c('GroupType','LexiconType','GroupNumber','Generation','Unique_language','Pair', 'Unique_Participant','New_Redup_B_mean'))
df_homonymy$Word_repetition<-NA

for(i in unique(df_homonymy$Unique_language)){
  subs_ <- subset(df_director_generations_1_to_5,Unique_language==i)
  for(j in 1:nrow(df_homonymy)){
    if(toString(df_homonymy[j,]$Unique_language) == i){
      df_homonymy[j,]$Word_repetition <- (length(subs_$TypedLabel)-length(unique(subs_$TypedLabel)))/length(subs_$TypedLabel)
      
    }
  }
}

initial_languages<-subset(df_director,Generation==0)
df_initial_languages <-setNames(aggregate(initial_languages$New_Redup_B, by=list(initial_languages$GroupType,initial_languages$LexiconType,initial_languages$GroupNumber,initial_languages$Generation,initial_languages$Unique_language,initial_languages$Pair, initial_languages$Unique_Participant), FUN=mean),c('GroupType','LexiconType','GroupNumber','Generation','Unique_language','Pair', 'Unique_Participant','New_Redup_B_mean'))
df_initial_languages$Word_repetition <-0
df_homonymy<-rbind(df_initial_languages,df_homonymy)

df_homonymy_1_to_5 <- subset(df_homonymy, Generation>0) #generations 1 to 5
cor(df_homonymy_1_to_5$New_Redup_B_mean,df_homonymy_1_to_5$Word_repetition) #looking at mean CVC redup. When looking at sum CVC redup the correlation was .20...

df_homonymy$timebin = df_homonymy$Generation + 1
t_ = poly(unique(df_homonymy$timebin), 3)
df_homonymy[,paste("ot", 1:3, sep="")] <- t_[df_homonymy$timebin, 1:3]

contrasts(df_homonymy$LexiconType) = contr.sum(2)
contrasts(df_homonymy$GroupType) = contr.sum(2)
```

## Homonymy: Models
Can't fit a 3rd order model because there are fewer observations than random effects
```{r}
homonymy_first_order <- lmer(Word_repetition ~
                          ot1*GroupType*LexiconType +
                          (1|Unique_Participant),
                        data=df_homonymy, control=lmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=2e5)))


homonymy_second_order <- lmer(Word_repetition ~
                          (ot1+ot2)*GroupType*LexiconType +
                            (1|Unique_Participant),
                        data=df_homonymy, control=lmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=2e5)))
summary(homonymy_first_order)
summary(homonymy_second_order)
```

## Homonymy: Model comparison
```{r}
homonyms_model.names <- c("Homonymy first order","Homonymy second order")
homonyms_summ.table <- do.call(rbind, lapply(list(homonymy_first_order,homonymy_second_order), broom::glance))
homonyms_table.cols <- c("df.residual", "deviance", "AIC")
homonyms_reported.table <- homonyms_summ.table[homonyms_table.cols]
names(homonyms_reported.table) <- c("Resid. Df", "Resid.Dev", "AIC")
homonyms_reported.table[['dAIC']] <-  with(homonyms_reported.table, AIC - min(AIC))
homonyms_reported.table[['AIC_weight']] <- with(homonyms_reported.table, exp(- 0.5 * dAIC) / sum(exp(- 0.5 * dAIC)))
homonyms_reported.table$AIC <- NULL
homonyms_reported.table$AIC_weight <- round(homonyms_reported.table$AIC_weight, 2)
homonyms_reported.table$dAIC <- round(homonyms_reported.table$dAIC, 1)
homonyms_reported.table$Resid.Dev <- round(homonyms_reported.table$Resid.Dev, 2)
row.names(homonyms_reported.table) <- homonyms_model.names
View(homonyms_reported.table)
anova(homonymy_first_order,homonymy_second_order)
sjPlot::tab_df(homonyms_reported.table,
               file="model comparison homonyms_.doc")

sjPlot::tab_df(round(coef(summary(homonymy_first_order)),3),file='homonyms_fixed_effects.doc')
```


## Homonymy: Plots showing model fit of 1st and 2nd order models by group type
```{r}
ggplot(df_homonymy, aes(Generation, Word_repetition, shape=GroupType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(homonymy_first_order), linetype=GroupType),
               fun.y=mean, geom="line") +
  ylab("Proportion of homonymy") 
dev.print(pdf,'Homonymy by group type - first order model.pdf')

ggplot(df_homonymy, aes(Generation, Word_repetition, color=GroupType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(homonymy_second_order), linetype=GroupType),
               fun.y=mean, geom="line") +
  ylab("Proportion of homonymy") 
dev.print(pdf,'Homonymy by group type - second order model.pdf')
```


## Homonymy: Plots showing model fit of 1st and 2nd order models by lexicon type
```{r}
ggplot(df_homonymy, aes(Generation, Word_repetition, shape=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(homonymy_first_order), linetype=LexiconType),
               fun.y=mean, geom="line") +
  ylab("Proportion of homonymy")
dev.print(pdf,'Homonymy by lexicon type - first order model.pdf')

ggplot(df_homonymy, aes(Generation, Word_repetition, color=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(homonymy_second_order), linetype=LexiconType),
               fun.y=mean, geom="line") +
  ylab("Proportion of homonymy") 
dev.print(pdf,'Homonymy by lexicon type - second order model.pdf')
```

# Homonymy: Logistic analysis

Analyze homonymy again, using 0/1 coding for word repetition and binomial stats

## Homonymy (log): Models
```{r}
df_homonymy$Word_repetition_binary = ifelse(df_homonymy$Word_repetition > 0, 1, 0)

homonymy_first_order_log <- glmer(Word_repetition_binary ~
                               ot1*GroupType*LexiconType + (1|Unique_Participant),
                             data=df_homonymy, family=binomial, 
                             control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))

homonymy_second_order_log <- glmer(Word_repetition_binary ~
                                    (ot1+ot2)*GroupType*LexiconType + (1|Unique_Participant),
                                  data=df_homonymy, family=binomial, 
                                  control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))


homonymy_third_order_log <- glmer(Word_repetition_binary ~
                                     (ot1+ot2+ot3)*GroupType*LexiconType + (1|Unique_Participant),
                                   data=df_homonymy, family=binomial, 
                                  control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))

summary(homonymy_first_order_log)
summary(homonymy_second_order_log)
summary(homonymy_third_order_log)
```
```{r}
numcols <- grep("^c\\.",names(df_homonymy))
dfs_homonymy <- df_homonymy
dfs_homonymy[,numcols] <- scale(dfs_homonymy[,numcols])
homonymy_first_order_log_sc <- update(homonymy_first_order_log, data=dfs_homonymy)
homonymy_second_order_log_sc <- update(homonymy_second_order_log, data=dfs_homonymy)

```


## Homonymy (log): Model comparison
Model comparison shows that the second-order model has the best fit.
```{r}
homonyms_log_model.names <- c("Homonymy first order log","Homonymy second order log", "Homonymy third order log")
homonyms_log_summ.table <- do.call(rbind, lapply(list(homonymy_first_order_log,homonymy_second_order, homonymy_third_order_log), broom::glance))
homonyms_log_table.cols <- c("df.residual", "deviance", "AIC")
homonyms_log_reported.table <- homonyms_log_summ.table[homonyms_log_table.cols]
names(homonyms_log_reported.table) <- c("Resid. Df", "Resid.Dev", "AIC")
homonyms_log_reported.table[['dAIC']] <-  with(homonyms_log_reported.table, AIC - min(AIC))
homonyms_log_reported.table[['AIC_weight']] <- with(homonyms_log_reported.table, exp(- 0.5 * dAIC) / sum(exp(- 0.5 * dAIC)))
homonyms_log_reported.table$AIC <- NULL
homonyms_log_reported.table$AIC_weight <- round(homonyms_log_reported.table$AIC_weight, 2)
homonyms_log_reported.table$dAIC <- round(homonyms_log_reported.table$dAIC, 1)
homonyms_log_reported.table$Resid.Dev <- round(homonyms_log_reported.table$Resid.Dev, 2)
row.names(homonyms_log_reported.table) <- homonyms_log_model.names
View(homonyms_log_reported.table)
anova(homonymy_first_order_log,homonymy_second_order_log, homonymy_third_order_log)
sjPlot::tab_df(homonyms_log_reported.table,
               file="model comparison homonyms log_.doc")

sjPlot::tab_df(round(coef(summary(homonymy_first_order_log)),3),file='homonyms_log_fixed_effects.doc')
homonyms_log_reported.table

```

## Homonymy (log): Plot showing model fit of 2nd order models by group type x lexicon size

```{r}
ggplot(df_homonymy, aes(Generation, Word_repetition_binary, shape=GroupType, linetype=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(homonymy_second_order_log), linetype=LexiconType),
               fun.y=mean, geom="line") +
  ylab("Proportion of homonymy")
dev.print(pdf,'Homonymy Log by Group and Lexicon type - first order model.pdf')
```

# Homonymy (Gen1-5)
Because there's discontinuity between Generation/Round 0 and 1, redo the analysis in 'Homonymy' without Generation/Round 0 (i.e., only Generations 1-5).

## Homonymy (Gen1-5): Models
Can't fit a 3rd order model because there are fewer observations than random effects
```{r}

homonymy_first_order_1to5 <- lmer(Word_repetition ~
                               ot1*GroupType*LexiconType + (1|Unique_Participant),
                             data=df_homonymy[df_homonymy$Generation != 0, ], 
                             control=lmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))


homonymy_second_order_1to5 <- lmer(Word_repetition ~
                                (ot1+ot2)*GroupType*LexiconType + (1|Unique_Participant),
                              data=df_homonymy[df_homonymy$Generation != 0, ], 
                              control=lmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
summary(homonymy_first_order_1to5)
summary(homonymy_second_order_1to5)
```

## Homonymy (Gen1-5): Model comparison
```{r}
homonyms_1to5_model.names <- c("Homonymy first order 1 to 5","Homonymy second order 1 to 5")
homonyms_1to5_summ.table <- do.call(rbind, lapply(list(homonymy_first_order_1to5,homonymy_second_order_1to5), broom::glance))
homonyms_1to5_table.cols <- c("df.residual", "deviance", "AIC")
homonyms_1to5_reported.table <- homonyms_1to5_summ.table[homonyms_1to5_table.cols]
names(homonyms_1to5_reported.table) <- c("Resid. Df", "Resid.Dev", "AIC")
homonyms_1to5_reported.table[['dAIC']] <-  with(homonyms_1to5_reported.table, AIC - min(AIC))
homonyms_1to5_reported.table[['AIC_weight']] <- with(homonyms_1to5_reported.table, exp(- 0.5 * dAIC) / sum(exp(- 0.5 * dAIC)))
homonyms_1to5_reported.table$AIC <- NULL
homonyms_1to5_reported.table$AIC_weight <- round(homonyms_1to5_reported.table$AIC_weight, 2)
homonyms_1to5_reported.table$dAIC <- round(homonyms_1to5_reported.table$dAIC, 1)
homonyms_1to5_reported.table$Resid.Dev <- round(homonyms_1to5_reported.table$Resid.Dev, 2)
row.names(homonyms_1to5_reported.table) <- homonyms_1to5_model.names
View(homonyms_1to5_reported.table)
anova(homonymy_first_order_1to5,homonymy_second_order_1to5)
sjPlot::tab_df(homonyms_reported.table,
               file="model comparison homonyms_1to5_.doc")

sjPlot::tab_df(round(coef(summary(homonymy_first_order_1to5)),3),file='homonyms_1to5_fixed_effects.doc')
```


## Homonymy (Gen1-5): Plots of model fit
```{r}
ggplot(df_homonymy[df_homonymy$Generation != 0, ], aes(Generation, Word_repetition, shape=GroupType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(homonymy_first_order_1to5), linetype=GroupType),
               fun.y=mean, geom="line") +
  ylab("Proportion of homonymy") 
dev.print(pdf,'Homonymy 1to5 by group type - first order model.pdf')

ggplot(df_homonymy[df_homonymy$Generation != 0, ], aes(Generation, Word_repetition, color=GroupType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(homonymy_second_order_1to5), linetype=GroupType),
               fun.y=mean, geom="line") +
  ylab("Proportion of homonymy") 
dev.print(pdf,'Homonymy 1to5  by group type - second order model.pdf')

ggplot(df_homonymy[df_homonymy$Generation != 0, ], aes(Generation, Word_repetition, shape=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(homonymy_first_order_1to5), linetype=LexiconType),
               fun.y=mean, geom="line") +
  ylab("Proportion of homonymy")
dev.print(pdf,'Homonymy 1to5 by lexicon type - first order model.pdf')

ggplot(df_homonymy[df_homonymy$Generation != 0, ], aes(Generation, Word_repetition, color=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(homonymy_second_order_1to5), linetype=LexiconType),
               fun.y=mean, geom="line") +
  ylab("Proportion of homonymy") 
dev.print(pdf,'Homonymy 1to5  by lexicon type - second order model.pdf')
```

# Homonymy and sound repetition

Two related questions: 1) Is sound repetition more prevelant among homonyms? (i.e., Is sound repetition driven by homonymy?) 2) Do we see the same pattern of sound repetition increase when we look only at non-homonyms?
```{r}
# This bit of script identifies homonyms in each lexicon
# Identify duplicates within every subset of words
df_u = ddply(df_director, c("GroupType", "LexiconType", "GroupNumber", "Generation", "Unique_language", "Pair", "Unique_Participant"), summarise, dup = duplicated(TypedLabel))
# Paste that information back to the main dateframe
df_u2 = cbind(df_director, df_u$dup)
# List up words that have copies (i.e., homonyms)
homs = unique(subset(df_u2, df_u$dup == 'TRUE'))
# Create variable 'homonym'
df_director = merge(df_director, homs, all.x=TRUE)
colnames(df_director)[52] <- "homonym"
df_director$homonym[is.na(df_director$homonym)] <- 0
# Create a subset without homonyms
NoHoms = subset(df_director, homonym == 0)
```

## Impact of homonymy on sound repetition
Is sound repetition more prevelant among homonyms? The analysis shows no effects of homonymy on CVC. So the answer is NO.
```{r}
CVC_hom_third_order <- glmer(New_Redup_B ~ #maximal random effect structure, up to cubic term 
                  (ot1+ot2+ot3) * GroupType * LexiconType * homonym + (1|Unique_Participant) + (1|Meaning),
                  data=df_director, family=binomial, 
                  control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))

CVC_hom_second_order <- glmer(New_Redup_B ~ #maximal random effect structure, up to quadratic term 
                    (ot1+ot2) * GroupType * LexiconType * homonym + (1|Unique_Participant) + (1|Meaning),
                   data=df_director, family=binomial, 
                   control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))

CVC_hom_first_order <- glmer(New_Redup_B ~ #maximal random effect structure, up to linear term                          
                    ot1 * GroupType * LexiconType * homonym+ (1|Unique_Participant) + (1|Meaning),
                    data=df_director, family=binomial, 
                    control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
summary(CVC_hom_third_order)
summary(CVC_hom_second_order)
summary(CVC_hom_first_order)
```
```{r}
CVC_hom_model.names <- c("CVC_hom first order","CVC_hom second order","CVC_hom third order")
CVC_hom_summ.table <- do.call(rbind, lapply(list(CVC_hom_first_order,CVC_hom_second_order,CVC_hom_third_order), broom::glance))
CVC_hom_table.cols <- c("df.residual", "deviance", "AIC")
CVC_hom_reported.table <- CVC_hom_summ.table[CVC_hom_table.cols]
names(CVC_hom_reported.table) <- c("Resid.Df", "Resid.Dev", "AIC")
CVC_hom_reported.table[['dAIC']] <-  with(CVC_hom_reported.table, AIC - min(AIC))
CVC_hom_reported.table[['AIC_weight']] <- with(CVC_hom_reported.table, exp(- 0.5 * dAIC) / sum(exp(- 0.5 * dAIC)))
CVC_hom_reported.table$AIC <- NULL
CVC_hom_reported.table$AIC_weight <- round(CVC_hom_reported.table$AIC_weight, 2)
CVC_hom_reported.table$dAIC <- round(CVC_hom_reported.table$dAIC, 1)
CVC_hom_reported.table$Resid.Dev <- round(CVC_hom_reported.table$Resid.Dev, 2)
row.names(CVC_hom_reported.table) <- CVC_hom_model.names
View(CVC_hom_reported.table)
sjPlot::tab_df(CVC_hom_reported.table,
       file="model comparison CVC_hom.doc")
```

## Sound repetition among non-homonyms
Here, an anlaysis is run using only words that don't have any homonyms. We get pretty much the same results as the analysis with all words. So sound repetition increases regardless of homonymy.
```{r}
CVC_NH_third_order <- glmer(New_Redup_B ~ #maximal random effect structure, up to cubic term 
                  (ot1+ot2+ot3) * GroupType * LexiconType + (1|Unique_Participant) + (1|Meaning),
                  data=df_director[df_director$homonym == 0, ], family=binomial, 
                  control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))

CVC_NH_second_order <- glmer(New_Redup_B ~ #maximal random effect structure, up to quadratic term 
                    (ot1+ot2) * GroupType * LexiconType + (1|Unique_Participant) + (1|Meaning),
                   data=df_director[df_director$homonym == 0, ], family=binomial, 
                   control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))

CVC_NH_first_order <- glmer(New_Redup_B ~ #maximal random effect structure, up to linear term                          
                    ot1 * GroupType * LexiconType + (1|Unique_Participant) + (1|Meaning),
                    data=df_director[df_director$homonym == 0, ], family=binomial, 
                    control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
summary(CVC_NH_third_order)
summary(CVC_NH_second_order)
summary(CVC_NH_first_order)
```

```{r}
CVC_NH_model.names <- c("CVC_NH first order","CVC_NH second order","CVC_NH third order")
CVC_NH_summ.table <- do.call(rbind, lapply(list(CVC_NH_first_order,CVC_NH_second_order,CVC_NH_third_order), broom::glance))
CVC_NH_table.cols <- c("df.residual", "deviance", "AIC")
CVC_NH_reported.table <- CVC_NH_summ.table[CVC_NH_table.cols]
names(CVC_NH_reported.table) <- c("Resid.Df", "Resid.Dev", "AIC")
CVC_NH_reported.table[['dAIC']] <-  with(CVC_NH_reported.table, AIC - min(AIC))
CVC_NH_reported.table[['AIC_weight']] <- with(CVC_NH_reported.table, exp(- 0.5 * dAIC) / sum(exp(- 0.5 * dAIC)))
CVC_NH_reported.table$AIC <- NULL
CVC_NH_reported.table$AIC_weight <- round(CVC_NH_reported.table$AIC_weight, 2)
CVC_NH_reported.table$dAIC <- round(CVC_NH_reported.table$dAIC, 1)
CVC_NH_reported.table$Resid.Dev <- round(CVC_NH_reported.table$Resid.Dev, 2)
row.names(CVC_NH_reported.table) <- CVC_NH_model.names
View(CVC_NH_reported.table)
sjPlot::tab_df(CVC_NH_reported.table,
       file="model comparison CVC_NH.doc")
```

Here's a plot showing the proportion of CVC repetition in non-homonyms
```{r}

ggplot(NoHoms, aes(Generation, New_Redup_B, shape=GroupType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(CVC_NH_first_order), linetype=GroupType),
               fun.y=mean, geom="line") +
  ylab("Consonant repetition")+ 
  ggtitle('Consonant repetition in non-homonyms by group type')
dev.print(pdf, 'Consonant repetition in non-homonyms by group type - first order model.pdf')

ggplot(NoHoms, aes(Generation, New_Redup_B, shape=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="pointrange") +
  stat_summary(aes(y=fitted(CVC_NH_first_order), linetype=LexiconType),
               fun.y=mean, geom="line") +
  ylab("Consonant repetition")+ 
  ggtitle('Consonant repetition in non-homonyms by lexicon size')
dev.print(pdf, 'Consonant repetition in non-homonyms by lexicon size - first order model.pdf')
```

# Word length and sound repetition
A query raised by Jenny is whether the repetition effects could be driven by word length - so longer words will be more likely to contain repetitions and words get longer in the chains than in the closed groups.

As the analysis below shows, there's obviously a clear association between word length and sound repetition, although the direction of the causality could be rep -> length.
```{r}
df_director_generations_1_to_5$LabelLength = nchar(as.character(df_director_generations_1_to_5$TypedLabel))
RepXLength = ddply(df_director_generations_1_to_5, c("LabelLength"), summarise, mean.rep = mean(New_Redup_B))
ggplot(RepXLength, aes(x=LabelLength, y=mean.rep)) +
    geom_point(shape=1) +    
    geom_smooth(method=lm) 
summary(lm(LabelLength ~ mean.rep, data=RepXLength))
```

So, can this explain why we find a GroupType or LexiconSize effect on repetition? Here's a figure showing the average label length by GroupType and LexiconSize, followed by a set of GCA analyses. The linear analysis (best fit) shows that label length increases over time, but this increase doesn't interact with GroupType or LexiconSize.
```{r message=FALSE, warning=FALSE}
df_director_plot$LabelLength = nchar(as.character(df_director$TypedLabel))
ggplot(df_director_plot, aes(Generation, LabelLength, group = group_id, colour=GroupType, linetype=LexiconType)) +
  stat_summary(fun.data=mean_se, geom="line") +
  ylab("Label length")+ 
  ggtitle('Label length by group type and lexicon size')
dev.print(pdf, 'Label length by group and lexicon.pdf')
```
```{r}
LabelLength_third_order <- lmer(LabelLength ~ #maximal random effect structure, up to cubic term 
                  (ot1+ot2+ot3) * GroupType * LexiconType + (1|Unique_Participant) + (1|Meaning),
                  data=df_director_plot, 
                  control=lmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))

LabelLength_second_order <- lmer(LabelLength ~ #maximal random effect structure, up to quadratic term 
                    (ot1+ot2) * GroupType * LexiconType + (1|Unique_Participant) + (1|Meaning),
                   data=df_director_plot, 
                   control=lmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))

LabelLength_first_order <- lmer(LabelLength ~ #maximal random effect structure, up to linear term                          
                    ot1 * GroupType * LexiconType + (1|Unique_Participant) + (1|Meaning),
                    data=df_director_plot,
                    control=lmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
summary(LabelLength_third_order)
summary(LabelLength_second_order)
summary(LabelLength_first_order)
```
```{r}
LabelLength_model.names <- c("LabelLength first order","LabelLength second order","LabelLength third order")
LabelLength_summ.table <- do.call(rbind, lapply(list(LabelLength_first_order,LabelLength_second_order,LabelLength_third_order), broom::glance))
LabelLength_table.cols <- c("df.residual", "deviance", "AIC")
LabelLength_reported.table <- LabelLength_summ.table[LabelLength_table.cols]
names(LabelLength_reported.table) <- c("Resid.Df", "Resid.Dev", "AIC")
LabelLength_reported.table[['dAIC']] <-  with(LabelLength_reported.table, AIC - min(AIC))
LabelLength_reported.table[['AIC_weight']] <- with(LabelLength_reported.table, exp(- 0.5 * dAIC) / sum(exp(- 0.5 * dAIC)))
LabelLength_reported.table$AIC <- NULL
LabelLength_reported.table$AIC_weight <- round(LabelLength_reported.table$AIC_weight, 2)
LabelLength_reported.table$dAIC <- round(LabelLength_reported.table$dAIC, 1)
LabelLength_reported.table$Resid.Dev <- round(LabelLength_reported.table$Resid.Dev, 2)
row.names(LabelLength_reported.table) <- LabelLength_model.names
View(LabelLength_reported.table)
sjPlot::tab_df(LabelLength_reported.table,
       file="model comparison LabelLength.doc")
```

For good measure, we can rerun our CVC analysis with LabelLength thrown in as a fixed effect (just the first-order analysis). Although LabelLength gobbles up the effects, there's still enough left in the Time x GroupType and Time x LexiconSize interactions to give us assurance that these factors cannot be explained away.
```{r}
CVC_LabelLength_first_order <- glmer(New_Redup_B ~ #maximal random effect structure, up to linear term                          
                    ot1 * GroupType * LexiconType + LabelLength + (1|Unique_Participant) + (1|Meaning),
                    data=df_director_plot, family=binomial, 
                    control=glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=1e5)))
summary(CVC_LabelLength_first_order)
```



Reduplication study: GCA analysis and model comparisons - New Coding

Aitor San Jose, Mits Ota, Kenny Smith

13 September 2019

1 Preamble

2 Packages

3 Data preparation

3.1 Data read-in

3.2 Sum-coding

3.3 Adding 3rd-order polynomials for GCA

4 Analysis of consonant repetition (“CVC”)

4.1 CVC: Descriptive plot

4.2 CVC: Full models

4.3 CVC: Reduced models

4.4 CVC: Model comparison

4.5 CVC: Plots showing model fit of 1st and 2nd order models by group type

4.6 CVC: Plots showing model fit of 1st and 2nd order models by lexicon type

5 Analysis of adjacent syllable repetition

5.1 Descriptive plot

5.2 Syllable repetition: Full models

5.3 Syllable repetition: Reduced models

5.4 Syllable repetition: Model comparison

5.5 Syllable repetition: Plots showing model fit of 1st and 2nd order models by group type

5.6 Syllable repetition: Plots showing model fit of 1st and 2nd order models by lexicon type

6 Accuracy

6.1 Descriptive plot

6.2 Accuracy: Full models

6.3 Accuracy: Reduced models

6.4 Accuracy: Model comparison

6.5 Accuracy: Plots showing model fit of 1st and 2nd order models by group type

6.6 Accuracy: Plots showing model fit of 1st and 2nd order models by lexicon type

7 Homonymy

7.1 Coding homonymy

7.2 Homonymy: Models

7.3 Homonymy: Model comparison

7.4 Homonymy: Plots showing model fit of 1st and 2nd order models by group type

7.5 Homonymy: Plots showing model fit of 1st and 2nd order models by lexicon type

8 Homonymy: Logistic analysis

8.1 Homonymy (log): Models

8.2 Homonymy (log): Model comparison

8.3 Homonymy (log): Plot showing model fit of 2nd order models by group type x lexicon size

9 Homonymy (Gen1-5)

9.1 Homonymy (Gen1-5): Models

9.2 Homonymy (Gen1-5): Model comparison

9.3 Homonymy (Gen1-5): Plots of model fit

10 Homonymy and sound repetition

10.1 Impact of homonymy on sound repetition

10.2 Sound repetition among non-homonyms

11 Word length and sound repetition